datasetId stringlengths 2 117 | card stringlengths 19 1.01M |
|---|---|
Rapando/TBL_KPIs | ---
license: apache-2.0
---
|
CyberHarem/projekt_red_arknights | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of projekt_red/レッド/红 (Arknights)
This is the dataset of projekt_red/レッド/红 (Arknights), containing 500 images and their tags.
The core tags of this character are `animal_ears, wolf_ears, grey_hair, hair_between_eyes, wolf_girl, yellow_eyes, tail, wolf_tail, long_hair, breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-----------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 500 | 824.97 MiB | [Download](https://huggingface.co/datasets/CyberHarem/projekt_red_arknights/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 396.38 MiB | [Download](https://huggingface.co/datasets/CyberHarem/projekt_red_arknights/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1216 | 863.70 MiB | [Download](https://huggingface.co/datasets/CyberHarem/projekt_red_arknights/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 692.67 MiB | [Download](https://huggingface.co/datasets/CyberHarem/projekt_red_arknights/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1216 | 1.32 GiB | [Download](https://huggingface.co/datasets/CyberHarem/projekt_red_arknights/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/projekt_red_arknights',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 16 |  |  |  |  |  | 1girl, fur-trimmed_hood, hooded_jacket, looking_at_viewer, red_jacket, solo, mask_around_neck, open_jacket, simple_background, upper_body, closed_mouth, hood_up, white_background, long_sleeves, grey_shirt |
| 1 | 5 |  |  |  |  |  | 1girl, fur-trimmed_hood, holding_knife, hood_up, hooded_jacket, long_sleeves, looking_at_viewer, mask_around_neck, open_jacket, red_jacket, solo, grey_shirt, upper_body, closed_mouth, holding_weapon, v-shaped_eyebrows |
| 2 | 8 |  |  |  |  |  | 1girl, black_pants, boots, fur-trimmed_hood, holding_knife, holding_weapon, hooded_jacket, long_sleeves, red_jacket, solo, black_footwear, cross-laced_footwear, open_jacket, grey_shirt, full_body, looking_at_viewer, hood_down, throwing_knife, coat, mask_around_neck |
| 3 | 33 |  |  |  |  |  | 1girl, official_alternate_costume, open_jacket, red_jacket, solo, black_one-piece_swimsuit, hooded_jacket, long_sleeves, ears_through_headwear, hood_up, simple_background, white_background, looking_at_viewer, casual_one-piece_swimsuit, cowboy_shot, closed_mouth, covered_navel, medium_breasts, thigh_strap, blush |
| 4 | 6 |  |  |  |  |  | 1girl, black_one-piece_swimsuit, blue_sky, casual_one-piece_swimsuit, day, hooded_jacket, long_sleeves, official_alternate_costume, open_jacket, outdoors, red_jacket, solo, closed_mouth, cowboy_shot, ears_through_headwear, hood_up, looking_at_viewer, soda_can, holding_can, covered_navel, drink_can, hand_in_pocket, medium_breasts |
| 5 | 7 |  |  |  |  |  | 1girl, black_one-piece_swimsuit, ears_through_headwear, hood_up, hooded_jacket, long_sleeves, official_alternate_costume, open_jacket, outdoors, red_jacket, beach_umbrella, blue_sky, casual_one-piece_swimsuit, day, solo, looking_at_viewer, cloud, cowboy_shot, hands_in_pockets, large_breasts, blush, covered_navel, dated, from_side, medium_breasts, thighs |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | fur-trimmed_hood | hooded_jacket | looking_at_viewer | red_jacket | solo | mask_around_neck | open_jacket | simple_background | upper_body | closed_mouth | hood_up | white_background | long_sleeves | grey_shirt | holding_knife | holding_weapon | v-shaped_eyebrows | black_pants | boots | black_footwear | cross-laced_footwear | full_body | hood_down | throwing_knife | coat | official_alternate_costume | black_one-piece_swimsuit | ears_through_headwear | casual_one-piece_swimsuit | cowboy_shot | covered_navel | medium_breasts | thigh_strap | blush | blue_sky | day | outdoors | soda_can | holding_can | drink_can | hand_in_pocket | beach_umbrella | cloud | hands_in_pockets | large_breasts | dated | from_side | thighs |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------------------|:----------------|:--------------------|:-------------|:-------|:-------------------|:--------------|:--------------------|:-------------|:---------------|:----------|:-------------------|:---------------|:-------------|:----------------|:-----------------|:--------------------|:--------------|:--------|:-----------------|:-----------------------|:------------|:------------|:-----------------|:-------|:-----------------------------|:---------------------------|:------------------------|:----------------------------|:--------------|:----------------|:-----------------|:--------------|:--------|:-----------|:------|:-----------|:-----------|:--------------|:------------|:-----------------|:-----------------|:--------|:-------------------|:----------------|:--------|:------------|:---------|
| 0 | 16 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 5 |  |  |  |  |  | X | X | X | X | X | X | X | X | | X | X | X | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 8 |  |  |  |  |  | X | X | X | X | X | X | X | X | | | | | | X | X | X | X | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 33 |  |  |  |  |  | X | | X | X | X | X | | X | X | | X | X | X | X | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | |
| 4 | 6 |  |  |  |  |  | X | | X | X | X | X | | X | | | X | X | | X | | | | | | | | | | | | | X | X | X | X | X | X | X | | | X | X | X | X | X | X | X | | | | | | | |
| 5 | 7 |  |  |  |  |  | X | | X | X | X | X | | X | | | | X | | X | | | | | | | | | | | | | X | X | X | X | X | X | X | | X | X | X | X | | | | | X | X | X | X | X | X | X |
|
germank/hh-rlhf_with_features_flan_t5_large | ---
dataset_info:
features:
- name: chosen
dtype: string
- name: rejected
dtype: string
- name: helpfulness_chosen
dtype: int64
- name: helpfulness_rejected
dtype: int64
- name: specificity_chosen
dtype: int64
- name: specificity_rejected
dtype: int64
- name: intent_chosen
dtype: int64
- name: intent_rejected
dtype: int64
- name: factuality_chosen
dtype: int64
- name: factuality_rejected
dtype: int64
- name: easy-to-understand_chosen
dtype: int64
- name: easy-to-understand_rejected
dtype: int64
- name: relevance_chosen
dtype: int64
- name: relevance_rejected
dtype: int64
- name: readability_chosen
dtype: int64
- name: readability_rejected
dtype: int64
- name: enough-detail_chosen
dtype: int64
- name: enough-detail_rejected
dtype: int64
- name: biased:_chosen
dtype: int64
- name: biased:_rejected
dtype: int64
- name: fail-to-consider-individual-preferences_chosen
dtype: int64
- name: fail-to-consider-individual-preferences_rejected
dtype: int64
- name: repetetive_chosen
dtype: int64
- name: repetetive_rejected
dtype: int64
- name: fail-to-consider-context_chosen
dtype: int64
- name: fail-to-consider-context_rejected
dtype: int64
- name: too-long_chosen
dtype: int64
- name: too-long_rejected
dtype: int64
- name: human
dtype: string
- name: assistant_chosen
dtype: string
- name: assistant_rejected
dtype: string
- name: log_score_chosen
dtype: float64
- name: log_score_rejected
dtype: float64
- name: labels
dtype: string
splits:
- name: train
num_bytes: 14434424
num_examples: 9574
- name: test
num_bytes: 14378349
num_examples: 9574
download_size: 15748504
dataset_size: 28812773
---
# Dataset Card for "hh-rlhf_with_features_flan_t5_large"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
anytp/conflicto | ---
license: apache-2.0
---
|
ashwathjadhav23/Dutch_MLM_3 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: text
dtype: string
splits:
- name: train
num_bytes: 54501690
num_examples: 25000
download_size: 32424106
dataset_size: 54501690
---
# Dataset Card for "Dutch_MLM_3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_130 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 1316796292.0
num_examples: 258601
download_size: 1344361029
dataset_size: 1316796292.0
---
# Dataset Card for "chunk_130"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
jholst/jkh-test-02 | ---
dataset_info:
features:
- name: title
dtype: string
- name: body
dtype: string
splits:
- name: train
num_bytes: 921
num_examples: 4
download_size: 2526
dataset_size: 921
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
sarp3d0n/wiki-typos | ---
license: apache-2.0
---
|
CyberHarem/tashkent_kantaicollection | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of tashkent/タシュケント (Kantai Collection)
This is the dataset of tashkent/タシュケント (Kantai Collection), containing 500 images and their tags.
The core tags of this character are `brown_hair, long_hair, twintails, low_twintails, hair_ornament, hairclip, brown_eyes, bow, hair_bow, black_bow, hair_between_eyes, hat, black_headwear, fur_hat, 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 | 563.07 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tashkent_kantaicollection/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 323.25 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tashkent_kantaicollection/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1235 | 709.79 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tashkent_kantaicollection/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 501.14 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tashkent_kantaicollection/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1235 | 1001.38 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tashkent_kantaicollection/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/tashkent_kantaicollection',
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 |  |  |  |  |  | 1girl, black_gloves, blue_shawl, fingerless_gloves, looking_at_viewer, papakha, simple_background, solo, star_(symbol), white_background, white_scarf, red_shirt, smile, torn_scarf, white_jacket, upper_body, anchor_necklace, blush, twitter_username |
| 1 | 5 |  |  |  |  |  | 1girl, anchor_necklace, black_belt, black_gloves, black_skirt, blue_shawl, fingerless_gloves, looking_at_viewer, papakha, red_shirt, ribbon_trim, simple_background, smile, solo, star_(symbol), torn_scarf, untucked_shirt, white_background, white_jacket, white_scarf, open_mouth, pantyhose, salute |
| 2 | 5 |  |  |  |  |  | 1girl, anchor_necklace, black_belt, black_footwear, black_gloves, black_skirt, blue_shawl, fingerless_gloves, looking_at_viewer, papakha, red_shirt, ribbon_trim, simple_background, smile, solo, star_(symbol), thigh_boots, thighhighs, torn_scarf, untucked_shirt, white_background, white_jacket, white_scarf, brown_pantyhose, cowboy_shot, twitter_username |
| 3 | 6 |  |  |  |  |  | 1girl, anchor_necklace, black_footwear, black_gloves, black_skirt, blue_shawl, fingerless_gloves, long_sleeves, looking_at_viewer, pantyhose, papakha, red_shirt, ribbon_trim, solo, star_(symbol), thigh_boots, thighhighs, torn_scarf, untucked_shirt, white_jacket, white_scarf, black_belt, open_mouth, blush, smile, pleated_skirt, red_background |
| 4 | 6 |  |  |  |  |  | 1girl, black_shirt, open_mouth, solo, black_gloves, black_skirt, blush, fingerless_gloves, looking_at_viewer, papakha, star_(symbol), white_background, long_sleeves, medium_breasts, simple_background, black_sweater, pleated_skirt, twitter_username |
| 5 | 8 |  |  |  |  |  | 1girl, looking_at_viewer, solo, simple_background, white_background, black_bikini, cleavage, navel, open_mouth, papakha, side-tie_bikini_bottom, star_(symbol), blush, large_breasts, black_thighhighs, blue_shawl, torn_scarf, white_scarf, black_gloves, collarbone, fingerless_gloves, medium_breasts |
| 6 | 8 |  |  |  |  |  | 1girl, alternate_costume, looking_at_viewer, medium_breasts, solo, cowboy_shot, open_mouth, blue_one-piece_swimsuit, blush, collarbone, gradient_background, school_swimsuit, name_tag, star_(symbol), blue_background, covered_navel, smile, twitter_username |
| 7 | 12 |  |  |  |  |  | 1girl, cloud, day, looking_at_viewer, solo, blue_sky, outdoors, cleavage, ocean, open_mouth, blush, collarbone, large_breasts, medium_breasts, smile, beach, blue_bikini, navel, water |
| 8 | 5 |  |  |  |  |  | 1girl, solo, white_background, competition_swimsuit, cowboy_shot, looking_at_viewer, simple_background, black_one-piece_swimsuit, large_breasts, blue_one-piece_swimsuit, highleg_swimsuit, medium_breasts, smile, twitter_username |
| 9 | 14 |  |  |  |  |  | alternate_costume, 1girl, solo, cleavage, medium_breasts, fake_animal_ears, looking_at_viewer, playboy_bunny, rabbit_ears, detached_collar, strapless_leotard, large_breasts, white_background, bowtie, open_mouth, simple_background, black_leotard, cowboy_shot, thighhighs, wrist_cuffs, brown_pantyhose, jacket, thigh_boots |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | black_gloves | blue_shawl | fingerless_gloves | looking_at_viewer | papakha | simple_background | solo | star_(symbol) | white_background | white_scarf | red_shirt | smile | torn_scarf | white_jacket | upper_body | anchor_necklace | blush | twitter_username | black_belt | black_skirt | ribbon_trim | untucked_shirt | open_mouth | pantyhose | salute | black_footwear | thigh_boots | thighhighs | brown_pantyhose | cowboy_shot | long_sleeves | pleated_skirt | red_background | black_shirt | medium_breasts | black_sweater | black_bikini | cleavage | navel | side-tie_bikini_bottom | large_breasts | black_thighhighs | collarbone | alternate_costume | blue_one-piece_swimsuit | gradient_background | school_swimsuit | name_tag | blue_background | covered_navel | cloud | day | blue_sky | outdoors | ocean | beach | blue_bikini | water | competition_swimsuit | black_one-piece_swimsuit | highleg_swimsuit | fake_animal_ears | playboy_bunny | rabbit_ears | detached_collar | strapless_leotard | bowtie | black_leotard | wrist_cuffs | jacket |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------------|:-------------|:--------------------|:--------------------|:----------|:--------------------|:-------|:----------------|:-------------------|:--------------|:------------|:--------|:-------------|:---------------|:-------------|:------------------|:--------|:-------------------|:-------------|:--------------|:--------------|:-----------------|:-------------|:------------|:---------|:-----------------|:--------------|:-------------|:------------------|:--------------|:---------------|:----------------|:-----------------|:--------------|:-----------------|:----------------|:---------------|:-----------|:--------|:-------------------------|:----------------|:-------------------|:-------------|:--------------------|:--------------------------|:----------------------|:------------------|:-----------|:------------------|:----------------|:--------|:------|:-----------|:-----------|:--------|:--------|:--------------|:--------|:-----------------------|:---------------------------|:-------------------|:-------------------|:----------------|:--------------|:------------------|:--------------------|:---------|:----------------|:--------------|:---------|
| 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 | X | X | X | X | X | X | X | | X | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 5 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | X | | X | X | X | X | X | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 6 |  |  |  |  |  | X | X | X | X | X | X | | X | X | | X | X | X | X | X | | X | X | | X | X | X | X | X | X | | X | X | X | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 6 |  |  |  |  |  | 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 | X | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 8 |  |  |  |  |  | X | | | | X | | | X | X | | | | X | | | | | X | X | | | | | X | | | | | | | X | | | | | X | | | | | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | |
| 7 | 12 |  |  |  |  |  | X | | | | X | | | X | | | | | X | | | | | X | | | | | | X | | | | | | | | | | | | X | | | X | X | | X | | X | | | | | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | |
| 8 | 5 |  |  |  |  |  | X | | | | X | | X | X | | X | | | X | | | | | | X | | | | | | | | | | | | X | | | | | X | | | | | | X | | | | X | | | | | | | | | | | | | | X | X | X | | | | | | | | | |
| 9 | 14 |  |  |  |  |  | X | | | | X | | X | X | | X | | | | | | | | | | | | | | X | | | | X | X | X | X | | | | | X | | | X | | | X | | | X | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X |
|
vietgpt/legal_citation_dataset | ---
dataset_info:
features:
- name: content
dtype: string
- name: citation
dtype: string
- name: meta
struct:
- name: effective_date
dtype: string
- name: issuing_agency
dtype: string
- name: promulgation_date
dtype: string
- name: sign_number
dtype: string
- name: signer
dtype: string
- name: type
dtype: string
- name: url
dtype: string
- name: text
dtype: string
splits:
- name: thong_tu
num_bytes: 441481674
num_examples: 103214
- name: thong_tu_detail
num_bytes: 536883374
num_examples: 291334
- name: luat
num_bytes: 87110456
num_examples: 31946
- name: luat_detail
num_bytes: 86293527
num_examples: 81851
- name: nghi_dinh
num_bytes: 333263187
num_examples: 91662
- name: nghi_dinh_detail
num_bytes: 360496955
num_examples: 238024
- name: train
num_bytes: 1826685967
num_examples: 828318
download_size: 1528560361
dataset_size: 3672215140
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: luat
path: data/luat-*
- split: nghi_dinh
path: data/nghi_dinh-*
- split: luat_detail
path: data/luat_detail-*
- split: nghi_dinh_detail
path: data/nghi_dinh_detail-*
- split: thong_tu
path: data/thong_tu-*
- split: thong_tu_detail
path: data/thong_tu_detail-*
---
|
CJWeiss/LGZ_eurlexsum | ---
dataset_info:
features:
- name: id
dtype: int64
- name: input
dtype: string
- name: output
dtype: string
- name: cluster
dtype: string
- name: old_id
dtype: int64
- name: length
dtype: int64
splits:
- name: train
num_bytes: 11363394
num_examples: 50
download_size: 4635052
dataset_size: 11363394
---
# Dataset Card for "LGZ_eurlexsum"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ovior/twitter_dataset_1713088450 | ---
dataset_info:
features:
- name: id
dtype: string
- name: tweet_content
dtype: string
- name: user_name
dtype: string
- name: user_id
dtype: string
- name: created_at
dtype: string
- name: url
dtype: string
- name: favourite_count
dtype: int64
- name: scraped_at
dtype: string
- name: image_urls
dtype: string
splits:
- name: train
num_bytes: 2870084
num_examples: 8280
download_size: 1659951
dataset_size: 2870084
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
lectura/naver_news_1024 | ---
dataset_info:
features:
- name: input_ids
sequence: int32
splits:
- name: train
num_bytes: 53074500
num_examples: 12945
download_size: 23902205
dataset_size: 53074500
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
task_categories:
- text-generation
language:
- ko
size_categories:
- 10K<n<100K
source_datasets:
- daekeun-ml/naver-news-summarization-ko
---
# Dataset Card for "naver_news_1024"
Preprocessed and tokenized [daekeun-ml/naver-news-summarization-ko](https://huggingface.co/datasets/daekeun-ml/naver-news-summarization-ko) dataset for pretraining. \
Concatenated all text and split into chunk size of **1024**. \
Used tokenizer from [beomi/llama-2-ko-7b](https://huggingface.co/beomi/llama-2-ko-7b)
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
atoomic/emoticonnect-sample | ---
license: artistic-2.0
task_categories:
- text-classification
language:
- fr
---
# Description
Data is using `.jsonl` format (each line is self isolated .json and can be parsed on its own).
Each row contains a text indexed by the key `content:` and some ratings split into groups.
* csp
* feeling
* gen
* persona
* sex
At this stage only the `feeling` group is filled.
Note: for now all vectors are filled with `0` value when mising.
This could change over time to save some space.
## Sample
Row example (pretty)
```json
{
"content": "...some text...",
"metadata":
{
"lng": "fr"
},
"rating":
{
},
"ratings":
{
"csp":
{
"c1": 0,
"c2": 0,
"c3": 0,
"c4": 0,
"c5": 0,
"c6": 0,
"c7": 0,
"c8": 0
},
"feeling":
{
"f1": 0,
"f2": 100,
"f3": 0,
"f4": 0,
"f5": 0,
"f6": 0,
"f7": 0,
"f8": 0
},
"gen":
{
"g1": 0,
"g2": 0,
"g3": 0,
"g4": 0
},
"persona":
{
"p1": 0,
"p2": 0,
"p3": 0,
"p4": 0,
"p5": 0,
"p6": 0,
"p7": 0,
"p8": 0
},
"sex":
{
"s1": 0,
"s2": 0
}
}
}
```
Note: more than a field can be set for a group
```json
{
"content": "...some text...",
"metadata":
{
"lng": "fr"
},
"rating":
{
},
"ratings":
{
"csp":
{
"c1": 0,
"c2": 0,
"c3": 0,
"c4": 0,
"c5": 0,
"c6": 0,
"c7": 0,
"c8": 0
},
"feeling":
{
"f1": 0,
"f2": 0,
"f3": 0,
"f4": 0,
"f5": 33.33,
"f6": 66.67,
"f7": 0,
"f8": 0
},
"gen":
{
"g1": 0,
"g2": 0,
"g3": 0,
"g4": 0
},
"persona":
{
"p1": 0,
"p2": 0,
"p3": 0,
"p4": 0,
"p5": 0,
"p6": 0,
"p7": 0,
"p8": 0
},
"sex":
{
"s1": 0,
"s2": 0
}
}
}
```
|
Blackroot/Tiny-Open-Domain-Books | ---
license: pddl
---
A tiny example dataset consisting of four books dedicated to the open domain in JSONL format:
* Alice in Wonderland - Lewis Caroll
* Dracula - Bram Stoker
* The Wonderful Wizard of Oz - L. Frank Baum
* The Count of Monte Cristo - Alexandre Dumas & Auguste Maquet
All works are open domain, thus this dataset is also dedicated to the open domain.
The dataset has been made to have extremely long context lengths, ideally as close to 2048 at possible without cutting off chunks in strange places. Each chunk is also overlapped by 100 characters from the prior chunk.
## Example:
There's an example LORA model here, which also includes training instructions for repoduction:
<https://huggingface.co/Blackroot/Llama2-13B-Lora-Tiny-Opendomain-Example> |
shidowake/augmxnt_ultra-orca-boros-en-ja-v1_split_11 | ---
dataset_info:
features:
- name: id
dtype: string
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
- name: weight
dtype: float64
- name: source
dtype: string
splits:
- name: train
num_bytes: 20639999.933149945
num_examples: 9397
download_size: 10721969
dataset_size: 20639999.933149945
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
CyberHarem/otonashi_kotori_theidolmster | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of otonashi_kotori/音無小鳥/오토나시코토리 (THE iDOLM@STER)
This is the dataset of otonashi_kotori/音無小鳥/오토나시코토리 (THE iDOLM@STER), containing 500 images and their tags.
The core tags of this character are `green_hair, short_hair, mole_under_mouth, mole, hairband, brown_eyes, breasts, red_eyes, yellow_hairband`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 500 | 423.16 MiB | [Download](https://huggingface.co/datasets/CyberHarem/otonashi_kotori_theidolmster/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 299.13 MiB | [Download](https://huggingface.co/datasets/CyberHarem/otonashi_kotori_theidolmster/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1095 | 591.81 MiB | [Download](https://huggingface.co/datasets/CyberHarem/otonashi_kotori_theidolmster/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 394.96 MiB | [Download](https://huggingface.co/datasets/CyberHarem/otonashi_kotori_theidolmster/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1095 | 740.10 MiB | [Download](https://huggingface.co/datasets/CyberHarem/otonashi_kotori_theidolmster/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/otonashi_kotori_theidolmster',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 10 |  |  |  |  |  | 1girl, blush, solo, zettai_ryouiki, pencil_skirt, black_thighhighs, open_mouth, headset, smile, one_eye_closed |
| 1 | 13 |  |  |  |  |  | 1girl, black_thighhighs, solo, smile, bow, blush, looking_at_viewer, pencil_skirt, open_mouth, zettai_ryouiki, vest |
| 2 | 11 |  |  |  |  |  | 1girl, long_sleeves, looking_at_viewer, open_mouth, pencil_skirt, solo, white_shirt, black_skirt, black_thighhighs, green_vest, :d, bangs, yellow_bowtie, zettai_ryouiki, blush, collared_shirt, standing, dress_shirt, miniskirt, simple_background, full_body, white_background, sandals |
| 3 | 6 |  |  |  |  |  | 1girl, green_vest, white_shirt, yellow_bowtie, bangs, blush, simple_background, solo, upper_body, white_background, long_sleeves, open_mouth, :d, looking_at_viewer |
| 4 | 7 |  |  |  |  |  | 1girl, solo, hair_flower, open_mouth, dress, necklace, :d, looking_at_viewer, medium_breasts |
| 5 | 15 |  |  |  |  |  | 1girl, looking_at_viewer, solo, bracelet, navel, open_mouth, blush, cleavage, hair_flower, yellow_bikini, :d, frilled_bikini, medium_breasts, necklace, bangs, collarbone, front-tie_top, side-tie_bikini_bottom, simple_background, white_background, cowboy_shot, large_breasts |
| 6 | 16 |  |  |  |  |  | 1boy, 1girl, hetero, solo_focus, blush, penis, sweat, thighhighs, nipples, sex, vaginal, large_breasts, open_mouth, female_pubic_hair, nude, cowgirl_position, girl_on_top, navel, spread_legs, cum_in_pussy, mosaic_censoring, smile |
| 7 | 8 |  |  |  |  |  | 1girl, playboy_bunny, rabbit_ears, solo, detached_collar, bowtie, wrist_cuffs, blush, leotard, smile, fishnet_pantyhose, large_breasts, open_mouth, rabbit_tail |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blush | solo | zettai_ryouiki | pencil_skirt | black_thighhighs | open_mouth | headset | smile | one_eye_closed | bow | looking_at_viewer | vest | long_sleeves | white_shirt | black_skirt | green_vest | :d | bangs | yellow_bowtie | collared_shirt | standing | dress_shirt | miniskirt | simple_background | full_body | white_background | sandals | upper_body | hair_flower | dress | necklace | medium_breasts | bracelet | navel | cleavage | yellow_bikini | frilled_bikini | collarbone | front-tie_top | side-tie_bikini_bottom | cowboy_shot | large_breasts | 1boy | hetero | solo_focus | penis | sweat | thighhighs | nipples | sex | vaginal | female_pubic_hair | nude | cowgirl_position | girl_on_top | spread_legs | cum_in_pussy | mosaic_censoring | playboy_bunny | rabbit_ears | detached_collar | bowtie | wrist_cuffs | leotard | fishnet_pantyhose | rabbit_tail |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:-------|:-----------------|:---------------|:-------------------|:-------------|:----------|:--------|:-----------------|:------|:--------------------|:-------|:---------------|:--------------|:--------------|:-------------|:-----|:--------|:----------------|:-----------------|:-----------|:--------------|:------------|:--------------------|:------------|:-------------------|:----------|:-------------|:--------------|:--------|:-----------|:-----------------|:-----------|:--------|:-----------|:----------------|:-----------------|:-------------|:----------------|:-------------------------|:--------------|:----------------|:-------|:---------|:-------------|:--------|:--------|:-------------|:----------|:------|:----------|:--------------------|:-------|:-------------------|:--------------|:--------------|:---------------|:-------------------|:----------------|:--------------|:------------------|:---------|:--------------|:----------|:--------------------|:--------------|
| 0 | 10 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 13 |  |  |  |  |  | X | X | X | X | X | X | X | | X | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 11 |  |  |  |  |  | X | X | X | X | X | X | X | | | | | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 6 |  |  |  |  |  | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 15 |  |  |  |  |  | X | X | X | | | | 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 | X | X | X | X | | | | | | | | |
| 7 | 8 |  |  |  |  |  | X | X | X | | | | X | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X |
|
CyberHarem/hina_hanetachi_areyoutheonlyonewholovesme | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of Hina Hanetachi (Are you the only one who loves me?)
This is the dataset of Hina Hanetachi (Are you the only one who loves me?), containing 479 images and their tags.
The core tags of this character are `blue_hair, ponytail, blue_eyes, 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 | 479 | 289.60 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hina_hanetachi_areyoutheonlyonewholovesme/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 1200 | 479 | 289.45 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hina_hanetachi_areyoutheonlyonewholovesme/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 844 | 471.65 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hina_hanetachi_areyoutheonlyonewholovesme/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/hina_hanetachi_areyoutheonlyonewholovesme',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 16 |  |  |  |  |  | 1girl, red_bowtie, sailor_collar, serafuku, solo, upper_body, indoors, long_sleeves, blush, looking_at_viewer |
| 1 | 6 |  |  |  |  |  | 1girl, long_sleeves, pleated_skirt, solo, open_mouth, serafuku, indoors, red_bow, sailor_collar |
| 2 | 11 |  |  |  |  |  | 1girl, indoors, pleated_skirt, sailor_collar, serafuku, short_sleeves, solo, sweatdrop, open_mouth, red_bowtie, smile, white_shirt, anime_coloring, bookshelf, collarbone |
| 3 | 5 |  |  |  |  |  | 1girl, blush, pleated_skirt, sailor_collar, serafuku, closed_eyes, holding, indoors, long_sleeves, smile, solo_focus |
| 4 | 12 |  |  |  |  |  | 1girl, pleated_skirt, serafuku, solo, smile, arms_behind_back, red_bow, short_sleeves |
| 5 | 9 |  |  |  |  |  | 1girl, ^_^, smile, blush, serafuku, solo |
| 6 | 10 |  |  |  |  |  | 1girl, blush, serafuku, solo, sailor_collar, open_mouth, long_hair, :d |
| 7 | 8 |  |  |  |  |  | 1girl, looking_at_viewer, serafuku, solo, smile, flower, short_hair, upper_body |
| 8 | 7 |  |  |  |  |  | 1girl, kneehighs, serafuku, solo, bench, pleated_skirt, sitting, black_socks, smile |
| 9 | 8 |  |  |  |  |  | closed_mouth, indoors, serafuku, blush, portrait, sailor_collar, short_hair, 2girls, blurry_background, classroom, solo_focus, 1girl, collarbone |
| 10 | 6 |  |  |  |  |  | 1girl, blurry, blush, close-up, from_side, portrait, profile, solo, short_hair, parted_lips |
| 11 | 7 |  |  |  |  |  | 1girl, blush, bookshelf, library, pleated_skirt, serafuku, short_sleeves, smile, solo_focus, arms_behind_back, blurry, red_bow, 1boy, indoors, open_mouth, bowtie, lamp, sailor_collar |
| 12 | 7 |  |  |  |  |  | 1girl, serafuku, solo, blush, indoors, sweatdrop, classroom, frown, clenched_hands, blurry, long_hair |
| 13 | 6 |  |  |  |  |  | 2girls, pink_hair, serafuku, hair_bobbles, skirt, short_hair, from_behind, pants, smile, twintails |
| 14 | 6 |  |  |  |  |  | black_hair, holding, long_hair, braid, long_sleeves, serafuku, 2girls, short_sleeves, 3girls, blue_sailor_collar, red_bowtie, white_shirt |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | red_bowtie | sailor_collar | serafuku | solo | upper_body | indoors | long_sleeves | blush | looking_at_viewer | pleated_skirt | open_mouth | red_bow | short_sleeves | sweatdrop | smile | white_shirt | anime_coloring | bookshelf | collarbone | closed_eyes | holding | solo_focus | arms_behind_back | ^_^ | long_hair | :d | flower | short_hair | kneehighs | bench | sitting | black_socks | closed_mouth | portrait | 2girls | blurry_background | classroom | blurry | close-up | from_side | profile | parted_lips | library | 1boy | bowtie | lamp | frown | clenched_hands | pink_hair | hair_bobbles | skirt | from_behind | pants | twintails | black_hair | braid | 3girls | blue_sailor_collar |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:-------------|:----------------|:-----------|:-------|:-------------|:----------|:---------------|:--------|:--------------------|:----------------|:-------------|:----------|:----------------|:------------|:--------|:--------------|:-----------------|:------------|:-------------|:--------------|:----------|:-------------|:-------------------|:------|:------------|:-----|:---------|:-------------|:------------|:--------|:----------|:--------------|:---------------|:-----------|:---------|:--------------------|:------------|:---------|:-----------|:------------|:----------|:--------------|:----------|:-------|:---------|:-------|:--------|:-----------------|:------------|:---------------|:--------|:--------------|:--------|:------------|:-------------|:--------|:---------|:---------------------|
| 0 | 16 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 6 |  |  |  |  |  | X | | X | X | X | | X | X | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 11 |  |  |  |  |  | X | X | X | X | X | | X | | | | X | X | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 5 |  |  |  |  |  | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 10 |  |  |  |  |  | X | | X | X | X | | | | X | | | X | | | | | | | | | | | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 8 |  |  |  |  |  | X | | | X | X | X | | | | X | | | | | | X | | | | | | | | | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 8 | 7 |  |  |  |  |  | X | | | X | X | | | | | | X | | | | | X | | | | | | | | | | | | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 9 | 8 |  |  |  |  |  | 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 | | | | | | | | | | | | | | | | |
| 11 | 7 |  |  |  |  |  | X | | X | X | | | X | | X | | X | 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 | | | | | | | | | | |
| 13 | 6 |  |  |  |  |  | | | | X | | | | | | | | | | | | X | | | | | | | | | | | | | X | | | | | | | X | | | | | | | | | | | | | | X | X | X | X | X | X | | | | |
| 14 | 6 |  |  |  |  |  | | X | | X | | | | X | | | | | | X | | | X | | | | | X | | | | X | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | X | X | X | X |
|
davanstrien/notebooks_by_user | ---
dataset_info:
features:
- name: user
dtype: large_string
- name: repo_notebook_count
dtype: int64
splits:
- name: train
num_bytes: 161739
num_examples: 6441
download_size: 86213
dataset_size: 161739
---
# Dataset Card for "notebooks_by_user"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
nakayama/DeDeDeDataset | ---
license: creativeml-openrail-m
---
# 概要
[DeDeDe](https://huggingface.co/nakayama/DeDeDe)の学習過程において利用された、
画像生成サービスやモデルを用いて作成された画像のセットのうち、
nsfwに相当すると判断したものを除いたものです。
# ライセンス
CreativeML OpenRAIL-M
|
davidpig/lnl-hausa | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: text
dtype: string
- name: clean_label
dtype: string
- name: clean_label_id
dtype: int64
- name: noisy_label
dtype: string
- name: noisy_label_id
dtype: int64
splits:
- name: train
num_bytes: 202396
num_examples: 2045
- name: validation
num_bytes: 28707
num_examples: 290
- name: test
num_bytes: 57551
num_examples: 582
download_size: 133910
dataset_size: 288654
---
# Dataset Card for "lnl-hausa"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
glaiveai/glaive-function-calling | ---
license: apache-2.0
task_categories:
- text-generation
language:
- en
size_categories:
- 10K<n<100K
---
This dataset consists of 52k samples generated through [Glaive](https://glaive.ai) for the task of function calling, in the following format-
```
SYSTEM: You are an helpful assistant who has access to the following functions to help the user, you can use the functions if needed-
{
JSON function definiton
}
USER: user message
ASSISTANT: assistant message
Function call invocations are formatted as-
ASSISTANT: <functioncall> {json function call}
Response to the function call is formatted as-
FUNCTION RESPONSE: {json function response}
```
There are also samples which do not have any function invocations, multiple invocations and samples with no functions presented and invoked to keep the data balanced. |
tollefj/sts15-sts-NOB | ---
license: cc-by-4.0
---
# Translated STS dataset to Norwegian Bokmål
Machine translated using the *No language left behind* model series, specifically the 1.3B variant: https://huggingface.co/facebook/nllb-200-distilled-1.3B |
ticoAg/tiger-sft-zh | ---
license: apache-2.0
language:
- zh
---
[Tigerbot](https://github.com/TigerResearch/TigerBot) 开源项目中微调中文sft-zh数据合集
本合集涵盖本组织下开源的其他中文sft-中文-数据集，不需要重复下载
## Usage
```python
import datasets
ds_sft = datasets.load_dataset('TigerResearch/sft_zh')
```
## 文件细分
| 类型 | 语言 | 数据集文件 | 数量|
| ------------ | ---- | -------------------------------------------------------------------------------------------------------------------------------- | ----------- |
| alpaca 中文 | 中文 | [tigerbot-alpaca-zh-0.5m](https://huggingface.co/datasets/TigerResearch/sft_zh/blob/main/tigerbot-alpaca-zh-0.5m.json) | 500k |
| 百科问答 | 中文 | [tigerbot-wiki-qa-1k](https://huggingface.co/datasets/TigerResearch/sft_zh/blob/main/tigerbot-wiki-qa-zh-1k.json) | 1k |
| 名著问答 | 中文 | [tigerbot-book-qa-1k](https://huggingface.co/datasets/TigerResearch/sft_zh/blob/main/tigerbot-book-qa-1k.json) | 1k |
| 猜谜语 | 中文 | [tigerbot-riddle-qa-1k](https://huggingface.co/datasets/TigerResearch/sft_zh/blob/main/tigerbot-riddle-qa-1k.json) | 1k |
| 阅读理解 | 中文 | [tigerbot-superclue-c3-zh-5k](https://huggingface.co/datasets/TigerResearch/sft_zh/blob/main/tigerbot-superclue-c3-zh-5k.json) | 5k |
| 问答 | 中文 | [tigerbot-hc3-zh-12k](https://huggingface.co/datasets/TigerResearch/sft_zh/blob/main/tigerbot-hc3-zh-12k.json) | 12k |
| 知乎问答 | 中文 | [tigerbot-zhihu-zh-10k](https://huggingface.co/datasets/TigerResearch/sft_zh/blob/main/tigerbot-zhihu-zh-10k.json) | 10k |
| 流萤sft | 中文 | [tigerbot-firefly-zh-20k](https://huggingface.co/datasets/TigerResearch/tigerbot-firefly-zh-20k) | 20k |
|
HuggingFaceM4/GQA | Invalid username or password. |
abacusai/MetaMathFewshot | ---
license: apache-2.0
---
A few-shot version of the MetaMath (https://huggingface.co/datasets/meta-math/MetaMathQA) dataset.
Each entry is formatted with 'question' and 'answer' keys. The 'question' key has a random number of query-answer pairs between 0 and 4 inclusive, before a final target query; the expected answer to this is stored in the content of 'answer'.
|
andersonbcdefg/lm_instruction_pairs | ---
dataset_info:
features:
- name: query
dtype: string
- name: pos
dtype: string
splits:
- name: train
num_bytes: 2732971333.327467
num_examples: 2401999
download_size: 534952510
dataset_size: 2732971333.327467
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
mattyamonaca/ParseDataset | ---
dataset_info:
features:
- name: image
dtype: image
- name: conditioning
dtype: image
- name: caption
dtype: string
splits:
- name: train
num_bytes: 751360758.0
num_examples: 413
download_size: 735118849
dataset_size: 751360758.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Seanxh/twitter_dataset_1713189169 | ---
dataset_info:
features:
- name: id
dtype: string
- name: tweet_content
dtype: string
- name: user_name
dtype: string
- name: user_id
dtype: string
- name: created_at
dtype: string
- name: url
dtype: string
- name: favourite_count
dtype: int64
- name: scraped_at
dtype: string
- name: image_urls
dtype: string
splits:
- name: train
num_bytes: 34627
num_examples: 78
download_size: 17893
dataset_size: 34627
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
liuyanchen1015/MULTI_VALUE_wnli_quotative_like | ---
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
- name: train
num_bytes: 560
num_examples: 4
download_size: 2759
dataset_size: 560
---
# Dataset Card for "MULTI_VALUE_wnli_quotative_like"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
budecosystem/intellecta | ---
license: apache-2.0
---
# Intellecta Cognitiva: Comprehensive Dataset for Academic Knowledge and Machine Reasoning
## Overview
The Intellecta a 11.53 billion tokens dataset mirrors human academic learning, encapsulating the progression from fundamental principles to complex topics as found in textbooks. It leverages structured prompts to guide AI in a human-like educational experience, ensuring that language models develop deep comprehension and generation capabilities reflective of nuanced human knowledge.
## Design Goals
Intellecta aims to:
- Improve language models' generalization ability
- Prevent model overfitting through diversity
- Emulate human learning processes
- Adhere to ethical data curation and open-source principles
## Data Sources
- **Textbook Data (30.5%)**: Sourced from scholarly publications.
- **Synthetic Data (69.5%)**: Encompasses programming, mathematics, NLP, reasoning, and various specialized domains.
*Figure 1: Distribution of Textbook and Synthetic Data in the Intellecta Dataset, highlighting the proportions of various domains within the synthetic subset.*
## Synthetic Data Generation
Utilizing the Mixtral-8x7B-Instruct-v0.1 model, the synthetic data is generated to stimulate complex thought processes and detailed explanations resembling textbook content.
## Dataset Curation
The dataset curation process includes:
- OCR content extraction
- Custom data-juicer pipeline for data recipes
- Deduplication using Simhash
- Toxicity filtering using Perspective API
- DBSCAN clustering for data diversity
## Cluster Analysis of Topics
The scatter plot below visualizes the semantic clustering of data topics within the dataset.
*Figure 3: Cluster Analysis of Topics in the Intellecta Dataset, highlighting the diversity and density of educational content.*
## Dataset Description
Intellecta Cognitiva contains over 100 topics, each rigorously selected for their educational value. It spans subjects from linear algebra to sentiment analysis and beyond.
## Evaluation Results
Here is a summary of the model's performance across different benchmarks:
| Model | Parameters | Token | ARC | HellaSwag | MMLU | Winogrande | GSM8K |
|-------------------------------------|------------|-------|-------|-----------|-------|------------|-------|
| EleutherAI/pythia-1b-deduped | 1.1B | - | 29.10 | 49.65 | 24.27 | 53.59 | 1.14 |
| facebook/opt-1.3b | 1.3B | 180B | 29.52 | 54.53 | 24.96 | 59.75 | 0.15 |
| Qwen/Qwen1.5-0.5B | 620M | - | 31.48 | 49.05 | 39.35 | 57.22 | 16.3 |
| HuggingFaceTB/cosmo-1b | 1.8B | 30B | 38.57 | 55.13 | 26.69 | 55.49 | 5.53 |
| TinyLlama/TinyLlama-1.1B-Chat-v0.6 | 1.1B | 3T | 31.66 | 55.79 | 25.98 | 59.35 | 2.12 |
| **boomer-634m** | **634M** | **11.5B** | **29.86** | **39.24** | **25.91** | **50.61** | **1.67** |
| EleutherAI/gpt-neo-1.3B | 1.3B | 380B | 31.23 | 48.47 | 24.82 | 56.91 | 0.45 |
The above table showcases the "boomer" model's robustness compared to other models with different parameter sizes and token counts. The results highlight the dataset's effectiveness in training high-quality language models.
## Conclusion
Intellecta is a step forward in AI research, providing high-quality, diverse data for language model training and potential for future enhancements in machine learning.
|
yemen2016/memo | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 298952996
num_examples: 3203876
download_size: 189354351
dataset_size: 298952996
---
# Dataset Card for "memo"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
nickponline/learning-segformer-dataset | ---
dataset_info:
features:
- name: pixel_values
dtype: image
- name: label
dtype: image
splits:
- name: train
num_bytes: 402128.0
num_examples: 100
download_size: 326407
dataset_size: 402128.0
---
# Dataset Card for "learning-segformer-dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Lienid/chat | ---
dataset_info:
features:
- name: question
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 375359448
num_examples: 200000
download_size: 191619983
dataset_size: 375359448
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
mdmoor/smmile | ---
license: cc-by-nc-sa-4.0
---
|
aisyahhrazak/ms-news-harakahdaily | ---
language:
- ms
---
### Dataset Summary
- 45505 Scraped News Article From Harakah Daily From 2017 to 21st May 2023
- Nearly all malay , small portion in english
### Dataset Format
```
{"url": "...", "headline": "...", "content": [...,...]}
``` |
WendellPires/Keila2 | ---
license: openrail
---
|
Jingying/LoRA_VQA | ---
license: cc-by-sa-4.0
task_categories:
- visual-question-answering
--- |
autoevaluate/autoeval-staging-eval-project-Blaise-g__SumPubmed-24db4c9a-12575678 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- Blaise-g/SumPubmed
eval_info:
task: summarization
model: L-macc/autotrain-Biomedical_sc_summ-1217846144
metrics: []
dataset_name: Blaise-g/SumPubmed
dataset_config: Blaise-g--SumPubmed
dataset_split: test
col_mapping:
text: text
target: abstract
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: L-macc/autotrain-Biomedical_sc_summ-1217846144
* Dataset: Blaise-g/SumPubmed
* Config: Blaise-g--SumPubmed
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@L-macc](https://huggingface.co/L-macc) for evaluating this model. |
pawan2411/kdf_dev2 | ---
dataset_info:
features:
- name: sentence
dtype: string
- name: relation
dtype: string
splits:
- name: train
num_bytes: 38253.6
num_examples: 99
- name: test
num_bytes: 296
num_examples: 1
download_size: 17713
dataset_size: 38549.6
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
BaekRok/kb_stt_data | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: audio
dtype: audio
- name: transcription
dtype: string
splits:
- name: train
num_bytes: 32039652658.296
num_examples: 547198
- name: test
num_bytes: 476820708.01
num_examples: 7051
download_size: 36376973349
dataset_size: 32516473366.306
---
# Dataset Card for "kb_stt_data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
zyznull/dureader-retrieval-corpus | ---
license: apache-2.0
---
# dureader
数据来自DuReader-Retreval数据集，这里是[原始地址](https://github.com/baidu/DuReader/tree/master/DuReader-Retrieval)。
> 本数据集只用作学术研究使用。如果本仓库涉及侵权行为，会立即删除。
|
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/01dafcef | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 186
num_examples: 10
download_size: 1342
dataset_size: 186
---
# Dataset Card for "01dafcef"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
FidelOdok/DOA_datasetSmall | ---
license: creativeml-openrail-m
dataset_info:
features:
- name: audio
dtype: audio
- name: label
dtype:
class_label:
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splits:
- name: train
num_bytes: 9688788846.77601
num_examples: 25148
download_size: 9690107170
dataset_size: 9688788846.77601
---
|
OmeletY/GeminiPro | ---
license: apache-2.0
---
|
hk-kaden-kim/uzh-hs23-etsp-eval-single-nogrid-bar | ---
dataset_info:
features:
- name: image
dtype: image
- name: caption
dtype: string
splits:
- name: test
num_bytes: 5078088.0
num_examples: 100
download_size: 5042214
dataset_size: 5078088.0
---
# Dataset Card for "uzh-hs23-etsp-eval-single-nogrid-bar"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_abhishekchohan__Yi-9B-Forest-DPO-v1.0 | ---
pretty_name: Evaluation run of abhishekchohan/Yi-9B-Forest-DPO-v1.0
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [abhishekchohan/Yi-9B-Forest-DPO-v1.0](https://huggingface.co/abhishekchohan/Yi-9B-Forest-DPO-v1.0)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_abhishekchohan__Yi-9B-Forest-DPO-v1.0\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-03-22T01:27:19.283205](https://huggingface.co/datasets/open-llm-leaderboard/details_abhishekchohan__Yi-9B-Forest-DPO-v1.0/blob/main/results_2024-03-22T01-27-19.283205.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\": 0.6938196615104713,\n\
\ \"acc_stderr\": 0.0306924861973612,\n \"acc_norm\": 0.6998996156067266,\n\
\ \"acc_norm_stderr\": 0.03128264221947934,\n \"mc1\": 0.3537331701346389,\n\
\ \"mc1_stderr\": 0.01673781435884615,\n \"mc2\": 0.5098213449066409,\n\
\ \"mc2_stderr\": 0.015436716238501152\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5716723549488054,\n \"acc_stderr\": 0.014460496367599022,\n\
\ \"acc_norm\": 0.5981228668941979,\n \"acc_norm_stderr\": 0.014327268614578278\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5889265086636128,\n\
\ \"acc_stderr\": 0.004910229643262738,\n \"acc_norm\": 0.7859988050189205,\n\
\ \"acc_norm_stderr\": 0.00409289457841898\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \
\ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5925925925925926,\n\
\ \"acc_stderr\": 0.04244633238353228,\n \"acc_norm\": 0.5925925925925926,\n\
\ \"acc_norm_stderr\": 0.04244633238353228\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.7368421052631579,\n \"acc_stderr\": 0.03583496176361073,\n\
\ \"acc_norm\": 0.7368421052631579,\n \"acc_norm_stderr\": 0.03583496176361073\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.84,\n\
\ \"acc_stderr\": 0.03684529491774708,\n \"acc_norm\": 0.84,\n \
\ \"acc_norm_stderr\": 0.03684529491774708\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7169811320754716,\n \"acc_stderr\": 0.027724236492700918,\n\
\ \"acc_norm\": 0.7169811320754716,\n \"acc_norm_stderr\": 0.027724236492700918\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7916666666666666,\n\
\ \"acc_stderr\": 0.03396116205845335,\n \"acc_norm\": 0.7916666666666666,\n\
\ \"acc_norm_stderr\": 0.03396116205845335\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.53,\n \"acc_stderr\": 0.050161355804659205,\n \
\ \"acc_norm\": 0.53,\n \"acc_norm_stderr\": 0.050161355804659205\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.61,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\"\
: 0.61,\n \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.4,\n \"acc_stderr\": 0.049236596391733084,\n \
\ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.049236596391733084\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.7283236994219653,\n\
\ \"acc_stderr\": 0.0339175032232166,\n \"acc_norm\": 0.7283236994219653,\n\
\ \"acc_norm_stderr\": 0.0339175032232166\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.45098039215686275,\n \"acc_stderr\": 0.04951218252396264,\n\
\ \"acc_norm\": 0.45098039215686275,\n \"acc_norm_stderr\": 0.04951218252396264\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.8,\n \"acc_stderr\": 0.04020151261036845,\n \"acc_norm\": 0.8,\n\
\ \"acc_norm_stderr\": 0.04020151261036845\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.7148936170212766,\n \"acc_stderr\": 0.029513196625539355,\n\
\ \"acc_norm\": 0.7148936170212766,\n \"acc_norm_stderr\": 0.029513196625539355\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5,\n\
\ \"acc_stderr\": 0.047036043419179864,\n \"acc_norm\": 0.5,\n \
\ \"acc_norm_stderr\": 0.047036043419179864\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.7103448275862069,\n \"acc_stderr\": 0.03780019230438015,\n\
\ \"acc_norm\": 0.7103448275862069,\n \"acc_norm_stderr\": 0.03780019230438015\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.5793650793650794,\n \"acc_stderr\": 0.025424835086924006,\n \"\
acc_norm\": 0.5793650793650794,\n \"acc_norm_stderr\": 0.025424835086924006\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5238095238095238,\n\
\ \"acc_stderr\": 0.04467062628403273,\n \"acc_norm\": 0.5238095238095238,\n\
\ \"acc_norm_stderr\": 0.04467062628403273\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \
\ \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.04988876515698589\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.8387096774193549,\n\
\ \"acc_stderr\": 0.02092332700642329,\n \"acc_norm\": 0.8387096774193549,\n\
\ \"acc_norm_stderr\": 0.02092332700642329\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.5812807881773399,\n \"acc_stderr\": 0.03471192860518468,\n\
\ \"acc_norm\": 0.5812807881773399,\n \"acc_norm_stderr\": 0.03471192860518468\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.84,\n \"acc_stderr\": 0.03684529491774708,\n \"acc_norm\"\
: 0.84,\n \"acc_norm_stderr\": 0.03684529491774708\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7878787878787878,\n \"acc_stderr\": 0.031922715695483016,\n\
\ \"acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.031922715695483016\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.8838383838383839,\n \"acc_stderr\": 0.02282888177524938,\n \"\
acc_norm\": 0.8838383838383839,\n \"acc_norm_stderr\": 0.02282888177524938\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.9067357512953368,\n \"acc_stderr\": 0.02098685459328972,\n\
\ \"acc_norm\": 0.9067357512953368,\n \"acc_norm_stderr\": 0.02098685459328972\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.7615384615384615,\n \"acc_stderr\": 0.021606294494647727,\n\
\ \"acc_norm\": 0.7615384615384615,\n \"acc_norm_stderr\": 0.021606294494647727\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.4185185185185185,\n \"acc_stderr\": 0.03007801307502206,\n \
\ \"acc_norm\": 0.4185185185185185,\n \"acc_norm_stderr\": 0.03007801307502206\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.819327731092437,\n \"acc_stderr\": 0.024991964966600756,\n \
\ \"acc_norm\": 0.819327731092437,\n \"acc_norm_stderr\": 0.024991964966600756\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.39072847682119205,\n \"acc_stderr\": 0.03983798306659806,\n \"\
acc_norm\": 0.39072847682119205,\n \"acc_norm_stderr\": 0.03983798306659806\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8605504587155963,\n \"acc_stderr\": 0.014852421490033067,\n \"\
acc_norm\": 0.8605504587155963,\n \"acc_norm_stderr\": 0.014852421490033067\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.6712962962962963,\n \"acc_stderr\": 0.03203614084670058,\n \"\
acc_norm\": 0.6712962962962963,\n \"acc_norm_stderr\": 0.03203614084670058\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8725490196078431,\n \"acc_stderr\": 0.023405530480846315,\n \"\
acc_norm\": 0.8725490196078431,\n \"acc_norm_stderr\": 0.023405530480846315\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8396624472573839,\n \"acc_stderr\": 0.02388438092596567,\n \
\ \"acc_norm\": 0.8396624472573839,\n \"acc_norm_stderr\": 0.02388438092596567\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7488789237668162,\n\
\ \"acc_stderr\": 0.02910522083322462,\n \"acc_norm\": 0.7488789237668162,\n\
\ \"acc_norm_stderr\": 0.02910522083322462\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.8244274809160306,\n \"acc_stderr\": 0.03336820338476074,\n\
\ \"acc_norm\": 0.8244274809160306,\n \"acc_norm_stderr\": 0.03336820338476074\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.8264462809917356,\n \"acc_stderr\": 0.0345727283691767,\n \"acc_norm\"\
: 0.8264462809917356,\n \"acc_norm_stderr\": 0.0345727283691767\n },\n\
\ \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7777777777777778,\n\
\ \"acc_stderr\": 0.0401910747255735,\n \"acc_norm\": 0.7777777777777778,\n\
\ \"acc_norm_stderr\": 0.0401910747255735\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.754601226993865,\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\": 0.5267857142857143,\n\
\ \"acc_stderr\": 0.047389751192741546,\n \"acc_norm\": 0.5267857142857143,\n\
\ \"acc_norm_stderr\": 0.047389751192741546\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8543689320388349,\n \"acc_stderr\": 0.0349260647662379,\n\
\ \"acc_norm\": 0.8543689320388349,\n \"acc_norm_stderr\": 0.0349260647662379\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9102564102564102,\n\
\ \"acc_stderr\": 0.018724301741941667,\n \"acc_norm\": 0.9102564102564102,\n\
\ \"acc_norm_stderr\": 0.018724301741941667\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.77,\n \"acc_stderr\": 0.04229525846816506,\n \
\ \"acc_norm\": 0.77,\n \"acc_norm_stderr\": 0.04229525846816506\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8339719029374202,\n\
\ \"acc_stderr\": 0.0133064782430663,\n \"acc_norm\": 0.8339719029374202,\n\
\ \"acc_norm_stderr\": 0.0133064782430663\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7398843930635838,\n \"acc_stderr\": 0.02361867831006935,\n\
\ \"acc_norm\": 0.7398843930635838,\n \"acc_norm_stderr\": 0.02361867831006935\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4491620111731844,\n\
\ \"acc_stderr\": 0.01663583834163192,\n \"acc_norm\": 0.4491620111731844,\n\
\ \"acc_norm_stderr\": 0.01663583834163192\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7745098039215687,\n \"acc_stderr\": 0.02392915551735129,\n\
\ \"acc_norm\": 0.7745098039215687,\n \"acc_norm_stderr\": 0.02392915551735129\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7909967845659164,\n\
\ \"acc_stderr\": 0.02309314039837422,\n \"acc_norm\": 0.7909967845659164,\n\
\ \"acc_norm_stderr\": 0.02309314039837422\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.75,\n \"acc_stderr\": 0.02409347123262133,\n \
\ \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.02409347123262133\n \
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\"\
: 0.5531914893617021,\n \"acc_stderr\": 0.029658235097666904,\n \"\
acc_norm\": 0.5531914893617021,\n \"acc_norm_stderr\": 0.029658235097666904\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.48370273794002605,\n\
\ \"acc_stderr\": 0.012763450734699812,\n \"acc_norm\": 0.48370273794002605,\n\
\ \"acc_norm_stderr\": 0.012763450734699812\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.7132352941176471,\n \"acc_stderr\": 0.027472274473233818,\n\
\ \"acc_norm\": 0.7132352941176471,\n \"acc_norm_stderr\": 0.027472274473233818\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.704248366013072,\n \"acc_stderr\": 0.018463154132632817,\n \
\ \"acc_norm\": 0.704248366013072,\n \"acc_norm_stderr\": 0.018463154132632817\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7090909090909091,\n\
\ \"acc_stderr\": 0.04350271442923243,\n \"acc_norm\": 0.7090909090909091,\n\
\ \"acc_norm_stderr\": 0.04350271442923243\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7877551020408163,\n \"acc_stderr\": 0.026176967197866767,\n\
\ \"acc_norm\": 0.7877551020408163,\n \"acc_norm_stderr\": 0.026176967197866767\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8656716417910447,\n\
\ \"acc_stderr\": 0.024112678240900798,\n \"acc_norm\": 0.8656716417910447,\n\
\ \"acc_norm_stderr\": 0.024112678240900798\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.91,\n \"acc_stderr\": 0.028762349126466125,\n \
\ \"acc_norm\": 0.91,\n \"acc_norm_stderr\": 0.028762349126466125\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5421686746987951,\n\
\ \"acc_stderr\": 0.0387862677100236,\n \"acc_norm\": 0.5421686746987951,\n\
\ \"acc_norm_stderr\": 0.0387862677100236\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8304093567251462,\n \"acc_stderr\": 0.028782108105401705,\n\
\ \"acc_norm\": 0.8304093567251462,\n \"acc_norm_stderr\": 0.028782108105401705\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3537331701346389,\n\
\ \"mc1_stderr\": 0.01673781435884615,\n \"mc2\": 0.5098213449066409,\n\
\ \"mc2_stderr\": 0.015436716238501152\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7687450670876085,\n \"acc_stderr\": 0.01185004012485051\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.4836997725549659,\n \
\ \"acc_stderr\": 0.013765164147036954\n }\n}\n```"
repo_url: https://huggingface.co/abhishekchohan/Yi-9B-Forest-DPO-v1.0
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_03_22T01_27_19.283205
path:
- '**/details_harness|arc:challenge|25_2024-03-22T01-27-19.283205.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-03-22T01-27-19.283205.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_03_22T01_27_19.283205
path:
- '**/details_harness|gsm8k|5_2024-03-22T01-27-19.283205.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-03-22T01-27-19.283205.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_03_22T01_27_19.283205
path:
- '**/details_harness|hellaswag|10_2024-03-22T01-27-19.283205.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-03-22T01-27-19.283205.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_03_22T01_27_19.283205
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-22T01-27-19.283205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-22T01-27-19.283205.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-22T01-27-19.283205.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_03_22T01_27_19.283205
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-22T01-27-19.283205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-22T01-27-19.283205.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_03_22T01_27_19.283205
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-22T01-27-19.283205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-22T01-27-19.283205.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_03_22T01_27_19.283205
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-22T01-27-19.283205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-22T01-27-19.283205.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_03_22T01_27_19.283205
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-22T01-27-19.283205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-22T01-27-19.283205.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_03_22T01_27_19.283205
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-22T01-27-19.283205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-22T01-27-19.283205.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_03_22T01_27_19.283205
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-22T01-27-19.283205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-22T01-27-19.283205.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_03_22T01_27_19.283205
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-22T01-27-19.283205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-22T01-27-19.283205.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_03_22T01_27_19.283205
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-22T01-27-19.283205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-22T01-27-19.283205.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_03_22T01_27_19.283205
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-22T01-27-19.283205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-22T01-27-19.283205.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_03_22T01_27_19.283205
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-22T01-27-19.283205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-22T01-27-19.283205.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_03_22T01_27_19.283205
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-22T01-27-19.283205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-22T01-27-19.283205.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_03_22T01_27_19.283205
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-22T01-27-19.283205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-22T01-27-19.283205.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_03_22T01_27_19.283205
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-22T01-27-19.283205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-22T01-27-19.283205.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_03_22T01_27_19.283205
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-22T01-27-19.283205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-22T01-27-19.283205.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_03_22T01_27_19.283205
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-22T01-27-19.283205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-22T01-27-19.283205.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_03_22T01_27_19.283205
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-22T01-27-19.283205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-22T01-27-19.283205.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_03_22T01_27_19.283205
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-22T01-27-19.283205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-22T01-27-19.283205.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_03_22T01_27_19.283205
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-22T01-27-19.283205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-22T01-27-19.283205.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_03_22T01_27_19.283205
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-22T01-27-19.283205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-22T01-27-19.283205.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_03_22T01_27_19.283205
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-22T01-27-19.283205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-22T01-27-19.283205.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_03_22T01_27_19.283205
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-22T01-27-19.283205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-22T01-27-19.283205.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_03_22T01_27_19.283205
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-22T01-27-19.283205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-22T01-27-19.283205.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_03_22T01_27_19.283205
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-22T01-27-19.283205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-22T01-27-19.283205.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_03_22T01_27_19.283205
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-22T01-27-19.283205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-22T01-27-19.283205.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_03_22T01_27_19.283205
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-22T01-27-19.283205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-22T01-27-19.283205.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_03_22T01_27_19.283205
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-22T01-27-19.283205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-22T01-27-19.283205.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_03_22T01_27_19.283205
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-22T01-27-19.283205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-22T01-27-19.283205.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_03_22T01_27_19.283205
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-22T01-27-19.283205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-22T01-27-19.283205.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_03_22T01_27_19.283205
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-22T01-27-19.283205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-22T01-27-19.283205.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_03_22T01_27_19.283205
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-22T01-27-19.283205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-22T01-27-19.283205.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_03_22T01_27_19.283205
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-22T01-27-19.283205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-22T01-27-19.283205.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_03_22T01_27_19.283205
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-22T01-27-19.283205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-22T01-27-19.283205.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_03_22T01_27_19.283205
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-22T01-27-19.283205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-22T01-27-19.283205.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_03_22T01_27_19.283205
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-22T01-27-19.283205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-22T01-27-19.283205.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_03_22T01_27_19.283205
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-22T01-27-19.283205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-22T01-27-19.283205.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_03_22T01_27_19.283205
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-22T01-27-19.283205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-22T01-27-19.283205.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_03_22T01_27_19.283205
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-22T01-27-19.283205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-22T01-27-19.283205.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_03_22T01_27_19.283205
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-22T01-27-19.283205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-22T01-27-19.283205.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_03_22T01_27_19.283205
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-22T01-27-19.283205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-22T01-27-19.283205.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_03_22T01_27_19.283205
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-22T01-27-19.283205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-22T01-27-19.283205.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_03_22T01_27_19.283205
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-22T01-27-19.283205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-22T01-27-19.283205.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_03_22T01_27_19.283205
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-22T01-27-19.283205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-22T01-27-19.283205.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_03_22T01_27_19.283205
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-22T01-27-19.283205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-22T01-27-19.283205.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_03_22T01_27_19.283205
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-22T01-27-19.283205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-22T01-27-19.283205.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_03_22T01_27_19.283205
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-22T01-27-19.283205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-22T01-27-19.283205.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_03_22T01_27_19.283205
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-22T01-27-19.283205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-22T01-27-19.283205.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_03_22T01_27_19.283205
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-22T01-27-19.283205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-22T01-27-19.283205.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_03_22T01_27_19.283205
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-22T01-27-19.283205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-22T01-27-19.283205.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_03_22T01_27_19.283205
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-22T01-27-19.283205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-22T01-27-19.283205.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_03_22T01_27_19.283205
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-22T01-27-19.283205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-22T01-27-19.283205.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_03_22T01_27_19.283205
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-22T01-27-19.283205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-22T01-27-19.283205.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_03_22T01_27_19.283205
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-22T01-27-19.283205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-22T01-27-19.283205.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_03_22T01_27_19.283205
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-22T01-27-19.283205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-22T01-27-19.283205.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_03_22T01_27_19.283205
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-22T01-27-19.283205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-22T01-27-19.283205.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_03_22T01_27_19.283205
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-22T01-27-19.283205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-22T01-27-19.283205.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_03_22T01_27_19.283205
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-22T01-27-19.283205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-22T01-27-19.283205.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_03_22T01_27_19.283205
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-22T01-27-19.283205.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-22T01-27-19.283205.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_03_22T01_27_19.283205
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-22T01-27-19.283205.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-22T01-27-19.283205.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_03_22T01_27_19.283205
path:
- '**/details_harness|winogrande|5_2024-03-22T01-27-19.283205.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-03-22T01-27-19.283205.parquet'
- config_name: results
data_files:
- split: 2024_03_22T01_27_19.283205
path:
- results_2024-03-22T01-27-19.283205.parquet
- split: latest
path:
- results_2024-03-22T01-27-19.283205.parquet
---
# Dataset Card for Evaluation run of abhishekchohan/Yi-9B-Forest-DPO-v1.0
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [abhishekchohan/Yi-9B-Forest-DPO-v1.0](https://huggingface.co/abhishekchohan/Yi-9B-Forest-DPO-v1.0) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_abhishekchohan__Yi-9B-Forest-DPO-v1.0",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-03-22T01:27:19.283205](https://huggingface.co/datasets/open-llm-leaderboard/details_abhishekchohan__Yi-9B-Forest-DPO-v1.0/blob/main/results_2024-03-22T01-27-19.283205.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": 0.6938196615104713,
"acc_stderr": 0.0306924861973612,
"acc_norm": 0.6998996156067266,
"acc_norm_stderr": 0.03128264221947934,
"mc1": 0.3537331701346389,
"mc1_stderr": 0.01673781435884615,
"mc2": 0.5098213449066409,
"mc2_stderr": 0.015436716238501152
},
"harness|arc:challenge|25": {
"acc": 0.5716723549488054,
"acc_stderr": 0.014460496367599022,
"acc_norm": 0.5981228668941979,
"acc_norm_stderr": 0.014327268614578278
},
"harness|hellaswag|10": {
"acc": 0.5889265086636128,
"acc_stderr": 0.004910229643262738,
"acc_norm": 0.7859988050189205,
"acc_norm_stderr": 0.00409289457841898
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.34,
"acc_stderr": 0.04760952285695235,
"acc_norm": 0.34,
"acc_norm_stderr": 0.04760952285695235
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.5925925925925926,
"acc_stderr": 0.04244633238353228,
"acc_norm": 0.5925925925925926,
"acc_norm_stderr": 0.04244633238353228
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.7368421052631579,
"acc_stderr": 0.03583496176361073,
"acc_norm": 0.7368421052631579,
"acc_norm_stderr": 0.03583496176361073
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.84,
"acc_stderr": 0.03684529491774708,
"acc_norm": 0.84,
"acc_norm_stderr": 0.03684529491774708
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7169811320754716,
"acc_stderr": 0.027724236492700918,
"acc_norm": 0.7169811320754716,
"acc_norm_stderr": 0.027724236492700918
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7916666666666666,
"acc_stderr": 0.03396116205845335,
"acc_norm": 0.7916666666666666,
"acc_norm_stderr": 0.03396116205845335
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.53,
"acc_stderr": 0.050161355804659205,
"acc_norm": 0.53,
"acc_norm_stderr": 0.050161355804659205
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.61,
"acc_stderr": 0.04902071300001975,
"acc_norm": 0.61,
"acc_norm_stderr": 0.04902071300001975
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.4,
"acc_stderr": 0.049236596391733084,
"acc_norm": 0.4,
"acc_norm_stderr": 0.049236596391733084
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.7283236994219653,
"acc_stderr": 0.0339175032232166,
"acc_norm": 0.7283236994219653,
"acc_norm_stderr": 0.0339175032232166
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.45098039215686275,
"acc_stderr": 0.04951218252396264,
"acc_norm": 0.45098039215686275,
"acc_norm_stderr": 0.04951218252396264
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.8,
"acc_stderr": 0.04020151261036845,
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}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
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## Dataset Card Contact
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Spiderman01/2024_2_17 | ---
dataset_info:
features:
- name: train
dtype: string
- name: number
dtype: int64
- name: kind
dtype: string
splits:
- name: train
num_bytes: 738611
num_examples: 256
download_size: 289936
dataset_size: 738611
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
jonathandechert/20Minuten | ---
license: cc-by-4.0
---
|
eckendoerffer/news_fr | ---
license: cc-by-3.0
task_categories:
- text-generation
language:
- fr
tags:
- news
- media
- Press
size_categories:
- 1M<n<10M
---
# NEWS FR
There is an open-access [dataset on BnF / Gallica](https://transfert.bnf.fr/link/3a04ea3f-dbe8-4a4a-a302-913a89c3a7a8) comprising nearly a hundred newspapers from the print media spanning almost 100 years.
Unfortunately, for this dataset, only 85% of the text is transcribed accurately.
## DATASET
This dataset compiles 1M online articles from nearly 100 Francophone media outlets. This dataset is intended for research purposes and non-commercial use. It includes 1,140,000 lines for model training, and 63,500 lines for the test and validation files.
Included with this dataset are scripts to extract and process the article text from the same sources. The script is somewhat rough around the edges, but it is functional and commented.
### Format
- **Type**: Text
- **File Extension**: `.txt`
The text has been standardized for consistent formatting and line length. Additionally, the dataset has been filtered using the `langid` library to include only text in French.
### Structure
The dataset is divided into the following splits:
- `train.txt`: 2.2 GB - 1,140,000 rows - 90%
- `test.txt` : 122 MB - 63,500 rows - 5%
- `valid.txt`: 122 MB - 63,500 rows - 5%
### Exploring the Dataset
You can use the `explore_dataset.py` script to explore the dataset by randomly displaying a certain number of lines from it. The script creates and saves an index based on the line breaks, enabling faster data retrieval and display.
### Additional Information
This dataset is a subset of a larger 10GB French dataset, which also contains several thousand books and theses in French, Wikipedia, as well as several hundred thousand Francophone news articles.
## EXTRACT NEWS FR
The "NEWS FR" module allows for the extraction of online press articles from over a hundred different sources.
## Installation
To set up the module, follow the steps below:
1. **Database Setup**:
- Create a database and incorporate the two tables present in `database.sql`.
2. **Database Configuration**:
- Update your MySQL connection information in the `config.py` file.
3. **Dependencies Installation**:
- Install it using pip install:
```
pip install aiohttp mysql-connector-python beautifulsoup4 chardet colorama pyquery
```
## Usage
### 1_extract_rss.py:
This script fetches RSS feeds from various media outlets and adds URLs for further extraction.
```bash
python 1_extract_rss.py
```
### 2_extract_news.py:
This script retrieves the sources of articles for subsequent local processing.
```bash
python 2_extract_news.py
```
### 3_extract_news_txt.py:
This script extracts the text content of press articles and saves it (title + text) to a `.txt` file.
```bash
python 3_extract_news_txt.py
```
After completing this step, you can use the Python script located at /dataset/2_cleaning_txt.py to standardize the text for your dataset.
### 4_extract_news_url.py:
This script allows for the extraction of links to other articles from local article sources. This ensures swift retrieval of numerous past articles, as opposed to fetching only the most recent ones.
```bash
python 4_extract_news_url.py
```
After using this script, you'll need to run 2_extract_news.py again to retrieve the sources of the new articles, as well as 3_extract_news_txt.py to extract the text from these articles.
--- |
malteee/TruckDet2 | ---
dataset_info:
features:
- name: image_id
dtype: int64
- name: image
dtype: image
- name: width
dtype: int64
- name: height
dtype: int64
- name: objects
struct:
- name: area
sequence: float64
- name: bbox
sequence:
sequence: float64
- name: category
sequence: int64
- name: id
sequence: int64
splits:
- name: train
num_bytes: 78780289.0
num_examples: 651
- name: test
num_bytes: 3798987.0
num_examples: 82
download_size: 82582528
dataset_size: 82579276.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
# Dataset Card for "TruckDet2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
autoevaluate/autoeval-staging-eval-anli-plain_text-dfb10f-14405974 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- anli
eval_info:
task: natural_language_inference
model: MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli
metrics: []
dataset_name: anli
dataset_config: plain_text
dataset_split: test_r1
col_mapping:
text1: premise
text2: hypothesis
target: label
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Natural Language Inference
* Model: MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli
* Dataset: anli
* Config: plain_text
* Split: test_r1
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@MoritzLaurer](https://huggingface.co/MoritzLaurer) for evaluating this model. |
bmd1905/error-correction-vi | ---
license: apache-2.0
language:
- vi
--- |
Nolan1206/WhisperSmallTestmp3 | ---
dataset_info:
features:
- name: audio
dtype: audio
- name: sentence
dtype: string
splits:
- name: train
num_bytes: 138283843.39
num_examples: 2586
- name: test
num_bytes: 7987803.0
num_examples: 231
download_size: 134971232
dataset_size: 146271646.39
---
# Dataset Card for "WhisperSmallTestmp3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
abideen/Copilot | ---
dataset_info:
features:
- name: text
dtype: string
- name: id
dtype: string
- name: metadata
struct:
- name: file_path
dtype: string
- name: repo_id
dtype: string
- name: token_count
dtype: int64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 166992954.0
num_examples: 22966
download_size: 61641266
dataset_size: 166992954.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
open-llm-leaderboard/details_Nitral-AI__Prima-LelantaclesV7-experimentalv2-7b | ---
pretty_name: Evaluation run of Nitral-AI/Prima-LelantaclesV7-experimentalv2-7b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Nitral-AI/Prima-LelantaclesV7-experimentalv2-7b](https://huggingface.co/Nitral-AI/Prima-LelantaclesV7-experimentalv2-7b)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Nitral-AI__Prima-LelantaclesV7-experimentalv2-7b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-03-21T12:18:22.583517](https://huggingface.co/datasets/open-llm-leaderboard/details_Nitral-AI__Prima-LelantaclesV7-experimentalv2-7b/blob/main/results_2024-03-21T12-18-22.583517.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\": 0.6309246571274838,\n\
\ \"acc_stderr\": 0.032583549208812464,\n \"acc_norm\": 0.6334822296692221,\n\
\ \"acc_norm_stderr\": 0.03324159550807866,\n \"mc1\": 0.5177478580171359,\n\
\ \"mc1_stderr\": 0.017492470843075356,\n \"mc2\": 0.6813898639090257,\n\
\ \"mc2_stderr\": 0.014976157561060141\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6416382252559727,\n \"acc_stderr\": 0.014012883334859862,\n\
\ \"acc_norm\": 0.6808873720136519,\n \"acc_norm_stderr\": 0.013621696119173314\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6751643098984266,\n\
\ \"acc_stderr\": 0.004673563250946108,\n \"acc_norm\": 0.8586934873531169,\n\
\ \"acc_norm_stderr\": 0.0034762555096445346\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \
\ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6296296296296297,\n\
\ \"acc_stderr\": 0.04171654161354543,\n \"acc_norm\": 0.6296296296296297,\n\
\ \"acc_norm_stderr\": 0.04171654161354543\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6842105263157895,\n \"acc_stderr\": 0.0378272898086547,\n\
\ \"acc_norm\": 0.6842105263157895,\n \"acc_norm_stderr\": 0.0378272898086547\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.57,\n\
\ \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\": 0.57,\n \
\ \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7132075471698113,\n \"acc_stderr\": 0.027834912527544067,\n\
\ \"acc_norm\": 0.7132075471698113,\n \"acc_norm_stderr\": 0.027834912527544067\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7291666666666666,\n\
\ \"acc_stderr\": 0.03716177437566017,\n \"acc_norm\": 0.7291666666666666,\n\
\ \"acc_norm_stderr\": 0.03716177437566017\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.43,\n \"acc_stderr\": 0.04975698519562428,\n \
\ \"acc_norm\": 0.43,\n \"acc_norm_stderr\": 0.04975698519562428\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.51,\n \"acc_stderr\": 0.05024183937956911,\n \"acc_norm\": 0.51,\n\
\ \"acc_norm_stderr\": 0.05024183937956911\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\": 0.630057803468208,\n\
\ \"acc_stderr\": 0.0368122963339432,\n \"acc_norm\": 0.630057803468208,\n\
\ \"acc_norm_stderr\": 0.0368122963339432\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.30392156862745096,\n \"acc_stderr\": 0.045766654032077636,\n\
\ \"acc_norm\": 0.30392156862745096,\n \"acc_norm_stderr\": 0.045766654032077636\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.76,\n \"acc_stderr\": 0.04292346959909283,\n \"acc_norm\": 0.76,\n\
\ \"acc_norm_stderr\": 0.04292346959909283\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5574468085106383,\n \"acc_stderr\": 0.03246956919789958,\n\
\ \"acc_norm\": 0.5574468085106383,\n \"acc_norm_stderr\": 0.03246956919789958\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4824561403508772,\n\
\ \"acc_stderr\": 0.04700708033551038,\n \"acc_norm\": 0.4824561403508772,\n\
\ \"acc_norm_stderr\": 0.04700708033551038\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5724137931034483,\n \"acc_stderr\": 0.04122737111370333,\n\
\ \"acc_norm\": 0.5724137931034483,\n \"acc_norm_stderr\": 0.04122737111370333\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.40476190476190477,\n \"acc_stderr\": 0.025279850397404907,\n \"\
acc_norm\": 0.40476190476190477,\n \"acc_norm_stderr\": 0.025279850397404907\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.48412698412698413,\n\
\ \"acc_stderr\": 0.04469881854072606,\n \"acc_norm\": 0.48412698412698413,\n\
\ \"acc_norm_stderr\": 0.04469881854072606\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.43,\n \"acc_stderr\": 0.049756985195624284,\n \
\ \"acc_norm\": 0.43,\n \"acc_norm_stderr\": 0.049756985195624284\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.7645161290322581,\n \"acc_stderr\": 0.02413763242933771,\n \"\
acc_norm\": 0.7645161290322581,\n \"acc_norm_stderr\": 0.02413763242933771\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.5221674876847291,\n \"acc_stderr\": 0.03514528562175008,\n \"\
acc_norm\": 0.5221674876847291,\n \"acc_norm_stderr\": 0.03514528562175008\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.73,\n \"acc_stderr\": 0.044619604333847394,\n \"acc_norm\"\
: 0.73,\n \"acc_norm_stderr\": 0.044619604333847394\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7636363636363637,\n \"acc_stderr\": 0.03317505930009182,\n\
\ \"acc_norm\": 0.7636363636363637,\n \"acc_norm_stderr\": 0.03317505930009182\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7474747474747475,\n \"acc_stderr\": 0.03095405547036589,\n \"\
acc_norm\": 0.7474747474747475,\n \"acc_norm_stderr\": 0.03095405547036589\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8704663212435233,\n \"acc_stderr\": 0.02423353229775873,\n\
\ \"acc_norm\": 0.8704663212435233,\n \"acc_norm_stderr\": 0.02423353229775873\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6384615384615384,\n \"acc_stderr\": 0.024359581465396993,\n\
\ \"acc_norm\": 0.6384615384615384,\n \"acc_norm_stderr\": 0.024359581465396993\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.37777777777777777,\n \"acc_stderr\": 0.02956070739246572,\n \
\ \"acc_norm\": 0.37777777777777777,\n \"acc_norm_stderr\": 0.02956070739246572\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6848739495798319,\n \"acc_stderr\": 0.030176808288974337,\n\
\ \"acc_norm\": 0.6848739495798319,\n \"acc_norm_stderr\": 0.030176808288974337\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.3576158940397351,\n \"acc_stderr\": 0.03913453431177258,\n \"\
acc_norm\": 0.3576158940397351,\n \"acc_norm_stderr\": 0.03913453431177258\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8201834862385321,\n \"acc_stderr\": 0.016465345467391517,\n \"\
acc_norm\": 0.8201834862385321,\n \"acc_norm_stderr\": 0.016465345467391517\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.4861111111111111,\n \"acc_stderr\": 0.03408655867977749,\n \"\
acc_norm\": 0.4861111111111111,\n \"acc_norm_stderr\": 0.03408655867977749\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8137254901960784,\n \"acc_stderr\": 0.027325470966716312,\n \"\
acc_norm\": 0.8137254901960784,\n \"acc_norm_stderr\": 0.027325470966716312\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7721518987341772,\n \"acc_stderr\": 0.02730348459906943,\n \
\ \"acc_norm\": 0.7721518987341772,\n \"acc_norm_stderr\": 0.02730348459906943\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6905829596412556,\n\
\ \"acc_stderr\": 0.03102441174057221,\n \"acc_norm\": 0.6905829596412556,\n\
\ \"acc_norm_stderr\": 0.03102441174057221\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7404580152671756,\n \"acc_stderr\": 0.03844876139785271,\n\
\ \"acc_norm\": 0.7404580152671756,\n \"acc_norm_stderr\": 0.03844876139785271\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7768595041322314,\n \"acc_stderr\": 0.03800754475228733,\n \"\
acc_norm\": 0.7768595041322314,\n \"acc_norm_stderr\": 0.03800754475228733\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7129629629629629,\n\
\ \"acc_stderr\": 0.04373313040914761,\n \"acc_norm\": 0.7129629629629629,\n\
\ \"acc_norm_stderr\": 0.04373313040914761\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7607361963190185,\n \"acc_stderr\": 0.0335195387952127,\n\
\ \"acc_norm\": 0.7607361963190185,\n \"acc_norm_stderr\": 0.0335195387952127\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.44642857142857145,\n\
\ \"acc_stderr\": 0.04718471485219588,\n \"acc_norm\": 0.44642857142857145,\n\
\ \"acc_norm_stderr\": 0.04718471485219588\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7766990291262136,\n \"acc_stderr\": 0.04123553189891431,\n\
\ \"acc_norm\": 0.7766990291262136,\n \"acc_norm_stderr\": 0.04123553189891431\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8504273504273504,\n\
\ \"acc_stderr\": 0.023365051491753715,\n \"acc_norm\": 0.8504273504273504,\n\
\ \"acc_norm_stderr\": 0.023365051491753715\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \
\ \"acc_norm\": 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8071519795657727,\n\
\ \"acc_stderr\": 0.014108533515757431,\n \"acc_norm\": 0.8071519795657727,\n\
\ \"acc_norm_stderr\": 0.014108533515757431\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.6820809248554913,\n \"acc_stderr\": 0.025070713719153186,\n\
\ \"acc_norm\": 0.6820809248554913,\n \"acc_norm_stderr\": 0.025070713719153186\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4044692737430168,\n\
\ \"acc_stderr\": 0.01641444091729315,\n \"acc_norm\": 0.4044692737430168,\n\
\ \"acc_norm_stderr\": 0.01641444091729315\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7026143790849673,\n \"acc_stderr\": 0.02617390850671858,\n\
\ \"acc_norm\": 0.7026143790849673,\n \"acc_norm_stderr\": 0.02617390850671858\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.684887459807074,\n\
\ \"acc_stderr\": 0.02638527370346448,\n \"acc_norm\": 0.684887459807074,\n\
\ \"acc_norm_stderr\": 0.02638527370346448\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7191358024691358,\n \"acc_stderr\": 0.02500646975579921,\n\
\ \"acc_norm\": 0.7191358024691358,\n \"acc_norm_stderr\": 0.02500646975579921\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.475177304964539,\n \"acc_stderr\": 0.029790719243829714,\n \
\ \"acc_norm\": 0.475177304964539,\n \"acc_norm_stderr\": 0.029790719243829714\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.455019556714472,\n\
\ \"acc_stderr\": 0.012718456618701766,\n \"acc_norm\": 0.455019556714472,\n\
\ \"acc_norm_stderr\": 0.012718456618701766\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6286764705882353,\n \"acc_stderr\": 0.029349803139765873,\n\
\ \"acc_norm\": 0.6286764705882353,\n \"acc_norm_stderr\": 0.029349803139765873\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6241830065359477,\n \"acc_stderr\": 0.01959402113657744,\n \
\ \"acc_norm\": 0.6241830065359477,\n \"acc_norm_stderr\": 0.01959402113657744\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6454545454545455,\n\
\ \"acc_stderr\": 0.045820048415054174,\n \"acc_norm\": 0.6454545454545455,\n\
\ \"acc_norm_stderr\": 0.045820048415054174\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.689795918367347,\n \"acc_stderr\": 0.029613459872484375,\n\
\ \"acc_norm\": 0.689795918367347,\n \"acc_norm_stderr\": 0.029613459872484375\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8507462686567164,\n\
\ \"acc_stderr\": 0.02519692987482707,\n \"acc_norm\": 0.8507462686567164,\n\
\ \"acc_norm_stderr\": 0.02519692987482707\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.86,\n \"acc_stderr\": 0.034873508801977704,\n \
\ \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.034873508801977704\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5120481927710844,\n\
\ \"acc_stderr\": 0.03891364495835817,\n \"acc_norm\": 0.5120481927710844,\n\
\ \"acc_norm_stderr\": 0.03891364495835817\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8187134502923976,\n \"acc_stderr\": 0.029547741687640044,\n\
\ \"acc_norm\": 0.8187134502923976,\n \"acc_norm_stderr\": 0.029547741687640044\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.5177478580171359,\n\
\ \"mc1_stderr\": 0.017492470843075356,\n \"mc2\": 0.6813898639090257,\n\
\ \"mc2_stderr\": 0.014976157561060141\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8113654301499605,\n \"acc_stderr\": 0.0109951723180198\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.5223654283548143,\n \
\ \"acc_stderr\": 0.013758699485911838\n }\n}\n```"
repo_url: https://huggingface.co/Nitral-AI/Prima-LelantaclesV7-experimentalv2-7b
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_03_21T12_18_22.583517
path:
- '**/details_harness|arc:challenge|25_2024-03-21T12-18-22.583517.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-03-21T12-18-22.583517.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_03_21T12_18_22.583517
path:
- '**/details_harness|gsm8k|5_2024-03-21T12-18-22.583517.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-03-21T12-18-22.583517.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_03_21T12_18_22.583517
path:
- '**/details_harness|hellaswag|10_2024-03-21T12-18-22.583517.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-03-21T12-18-22.583517.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_03_21T12_18_22.583517
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-21T12-18-22.583517.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-21T12-18-22.583517.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-21T12-18-22.583517.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_03_21T12_18_22.583517
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-21T12-18-22.583517.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-21T12-18-22.583517.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_03_21T12_18_22.583517
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-21T12-18-22.583517.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-21T12-18-22.583517.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_03_21T12_18_22.583517
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-21T12-18-22.583517.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-21T12-18-22.583517.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_03_21T12_18_22.583517
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-21T12-18-22.583517.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-21T12-18-22.583517.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_03_21T12_18_22.583517
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-21T12-18-22.583517.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-21T12-18-22.583517.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_03_21T12_18_22.583517
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-21T12-18-22.583517.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-21T12-18-22.583517.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_03_21T12_18_22.583517
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-21T12-18-22.583517.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-21T12-18-22.583517.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_03_21T12_18_22.583517
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-21T12-18-22.583517.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-21T12-18-22.583517.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_03_21T12_18_22.583517
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-21T12-18-22.583517.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-21T12-18-22.583517.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_03_21T12_18_22.583517
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-21T12-18-22.583517.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-21T12-18-22.583517.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_03_21T12_18_22.583517
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-21T12-18-22.583517.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-21T12-18-22.583517.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_03_21T12_18_22.583517
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-21T12-18-22.583517.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-21T12-18-22.583517.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_03_21T12_18_22.583517
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-21T12-18-22.583517.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-21T12-18-22.583517.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_03_21T12_18_22.583517
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-21T12-18-22.583517.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-21T12-18-22.583517.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_03_21T12_18_22.583517
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-21T12-18-22.583517.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-21T12-18-22.583517.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_03_21T12_18_22.583517
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-21T12-18-22.583517.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-21T12-18-22.583517.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_03_21T12_18_22.583517
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-21T12-18-22.583517.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-21T12-18-22.583517.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_03_21T12_18_22.583517
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-21T12-18-22.583517.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-21T12-18-22.583517.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_03_21T12_18_22.583517
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-21T12-18-22.583517.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-21T12-18-22.583517.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_03_21T12_18_22.583517
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-21T12-18-22.583517.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-21T12-18-22.583517.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_03_21T12_18_22.583517
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-21T12-18-22.583517.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-21T12-18-22.583517.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_03_21T12_18_22.583517
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-21T12-18-22.583517.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-21T12-18-22.583517.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_03_21T12_18_22.583517
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-21T12-18-22.583517.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-21T12-18-22.583517.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_03_21T12_18_22.583517
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-21T12-18-22.583517.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-21T12-18-22.583517.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_03_21T12_18_22.583517
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-21T12-18-22.583517.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-21T12-18-22.583517.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_03_21T12_18_22.583517
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-21T12-18-22.583517.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-21T12-18-22.583517.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_03_21T12_18_22.583517
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-21T12-18-22.583517.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-21T12-18-22.583517.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_03_21T12_18_22.583517
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-21T12-18-22.583517.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-21T12-18-22.583517.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_03_21T12_18_22.583517
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-21T12-18-22.583517.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-21T12-18-22.583517.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_03_21T12_18_22.583517
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-21T12-18-22.583517.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-21T12-18-22.583517.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_03_21T12_18_22.583517
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-21T12-18-22.583517.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-21T12-18-22.583517.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_03_21T12_18_22.583517
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-21T12-18-22.583517.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-21T12-18-22.583517.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_03_21T12_18_22.583517
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-21T12-18-22.583517.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-21T12-18-22.583517.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_03_21T12_18_22.583517
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-21T12-18-22.583517.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-21T12-18-22.583517.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_03_21T12_18_22.583517
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-21T12-18-22.583517.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-21T12-18-22.583517.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_03_21T12_18_22.583517
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-21T12-18-22.583517.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-21T12-18-22.583517.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_03_21T12_18_22.583517
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-21T12-18-22.583517.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-21T12-18-22.583517.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_03_21T12_18_22.583517
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-21T12-18-22.583517.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-21T12-18-22.583517.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_03_21T12_18_22.583517
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-21T12-18-22.583517.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-21T12-18-22.583517.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_03_21T12_18_22.583517
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-21T12-18-22.583517.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-21T12-18-22.583517.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_03_21T12_18_22.583517
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-21T12-18-22.583517.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-21T12-18-22.583517.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_03_21T12_18_22.583517
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-21T12-18-22.583517.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-21T12-18-22.583517.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_03_21T12_18_22.583517
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-21T12-18-22.583517.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-21T12-18-22.583517.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_03_21T12_18_22.583517
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-21T12-18-22.583517.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-21T12-18-22.583517.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_03_21T12_18_22.583517
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-21T12-18-22.583517.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-21T12-18-22.583517.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_03_21T12_18_22.583517
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-21T12-18-22.583517.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-21T12-18-22.583517.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_03_21T12_18_22.583517
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-21T12-18-22.583517.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-21T12-18-22.583517.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_03_21T12_18_22.583517
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-21T12-18-22.583517.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-21T12-18-22.583517.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_03_21T12_18_22.583517
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-21T12-18-22.583517.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-21T12-18-22.583517.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_03_21T12_18_22.583517
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-21T12-18-22.583517.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-21T12-18-22.583517.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_03_21T12_18_22.583517
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-21T12-18-22.583517.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-21T12-18-22.583517.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_03_21T12_18_22.583517
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-21T12-18-22.583517.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-21T12-18-22.583517.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_03_21T12_18_22.583517
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-21T12-18-22.583517.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-21T12-18-22.583517.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_03_21T12_18_22.583517
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-21T12-18-22.583517.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-21T12-18-22.583517.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_03_21T12_18_22.583517
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-21T12-18-22.583517.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-21T12-18-22.583517.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_03_21T12_18_22.583517
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-21T12-18-22.583517.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-21T12-18-22.583517.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_03_21T12_18_22.583517
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-21T12-18-22.583517.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-21T12-18-22.583517.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_03_21T12_18_22.583517
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-21T12-18-22.583517.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-21T12-18-22.583517.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_03_21T12_18_22.583517
path:
- '**/details_harness|winogrande|5_2024-03-21T12-18-22.583517.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-03-21T12-18-22.583517.parquet'
- config_name: results
data_files:
- split: 2024_03_21T12_18_22.583517
path:
- results_2024-03-21T12-18-22.583517.parquet
- split: latest
path:
- results_2024-03-21T12-18-22.583517.parquet
---
# Dataset Card for Evaluation run of Nitral-AI/Prima-LelantaclesV7-experimentalv2-7b
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [Nitral-AI/Prima-LelantaclesV7-experimentalv2-7b](https://huggingface.co/Nitral-AI/Prima-LelantaclesV7-experimentalv2-7b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Nitral-AI__Prima-LelantaclesV7-experimentalv2-7b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-03-21T12:18:22.583517](https://huggingface.co/datasets/open-llm-leaderboard/details_Nitral-AI__Prima-LelantaclesV7-experimentalv2-7b/blob/main/results_2024-03-21T12-18-22.583517.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": 0.6309246571274838,
"acc_stderr": 0.032583549208812464,
"acc_norm": 0.6334822296692221,
"acc_norm_stderr": 0.03324159550807866,
"mc1": 0.5177478580171359,
"mc1_stderr": 0.017492470843075356,
"mc2": 0.6813898639090257,
"mc2_stderr": 0.014976157561060141
},
"harness|arc:challenge|25": {
"acc": 0.6416382252559727,
"acc_stderr": 0.014012883334859862,
"acc_norm": 0.6808873720136519,
"acc_norm_stderr": 0.013621696119173314
},
"harness|hellaswag|10": {
"acc": 0.6751643098984266,
"acc_stderr": 0.004673563250946108,
"acc_norm": 0.8586934873531169,
"acc_norm_stderr": 0.0034762555096445346
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.3,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.3,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6296296296296297,
"acc_stderr": 0.04171654161354543,
"acc_norm": 0.6296296296296297,
"acc_norm_stderr": 0.04171654161354543
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6842105263157895,
"acc_stderr": 0.0378272898086547,
"acc_norm": 0.6842105263157895,
"acc_norm_stderr": 0.0378272898086547
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.57,
"acc_stderr": 0.049756985195624284,
"acc_norm": 0.57,
"acc_norm_stderr": 0.049756985195624284
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7132075471698113,
"acc_stderr": 0.027834912527544067,
"acc_norm": 0.7132075471698113,
"acc_norm_stderr": 0.027834912527544067
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7291666666666666,
"acc_stderr": 0.03716177437566017,
"acc_norm": 0.7291666666666666,
"acc_norm_stderr": 0.03716177437566017
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.43,
"acc_stderr": 0.04975698519562428,
"acc_norm": 0.43,
"acc_norm_stderr": 0.04975698519562428
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.51,
"acc_stderr": 0.05024183937956911,
"acc_norm": 0.51,
"acc_norm_stderr": 0.05024183937956911
},
"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": 0.630057803468208,
"acc_stderr": 0.0368122963339432,
"acc_norm": 0.630057803468208,
"acc_norm_stderr": 0.0368122963339432
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.30392156862745096,
"acc_stderr": 0.045766654032077636,
"acc_norm": 0.30392156862745096,
"acc_norm_stderr": 0.045766654032077636
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.76,
"acc_stderr": 0.04292346959909283,
"acc_norm": 0.76,
"acc_norm_stderr": 0.04292346959909283
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5574468085106383,
"acc_stderr": 0.03246956919789958,
"acc_norm": 0.5574468085106383,
"acc_norm_stderr": 0.03246956919789958
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.4824561403508772,
"acc_stderr": 0.04700708033551038,
"acc_norm": 0.4824561403508772,
"acc_norm_stderr": 0.04700708033551038
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5724137931034483,
"acc_stderr": 0.04122737111370333,
"acc_norm": 0.5724137931034483,
"acc_norm_stderr": 0.04122737111370333
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.40476190476190477,
"acc_stderr": 0.025279850397404907,
"acc_norm": 0.40476190476190477,
"acc_norm_stderr": 0.025279850397404907
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.48412698412698413,
"acc_stderr": 0.04469881854072606,
"acc_norm": 0.48412698412698413,
"acc_norm_stderr": 0.04469881854072606
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.43,
"acc_stderr": 0.049756985195624284,
"acc_norm": 0.43,
"acc_norm_stderr": 0.049756985195624284
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7645161290322581,
"acc_stderr": 0.02413763242933771,
"acc_norm": 0.7645161290322581,
"acc_norm_stderr": 0.02413763242933771
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5221674876847291,
"acc_stderr": 0.03514528562175008,
"acc_norm": 0.5221674876847291,
"acc_norm_stderr": 0.03514528562175008
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.73,
"acc_stderr": 0.044619604333847394,
"acc_norm": 0.73,
"acc_norm_stderr": 0.044619604333847394
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7636363636363637,
"acc_stderr": 0.03317505930009182,
"acc_norm": 0.7636363636363637,
"acc_norm_stderr": 0.03317505930009182
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7474747474747475,
"acc_stderr": 0.03095405547036589,
"acc_norm": 0.7474747474747475,
"acc_norm_stderr": 0.03095405547036589
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8704663212435233,
"acc_stderr": 0.02423353229775873,
"acc_norm": 0.8704663212435233,
"acc_norm_stderr": 0.02423353229775873
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6384615384615384,
"acc_stderr": 0.024359581465396993,
"acc_norm": 0.6384615384615384,
"acc_norm_stderr": 0.024359581465396993
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.37777777777777777,
"acc_stderr": 0.02956070739246572,
"acc_norm": 0.37777777777777777,
"acc_norm_stderr": 0.02956070739246572
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6848739495798319,
"acc_stderr": 0.030176808288974337,
"acc_norm": 0.6848739495798319,
"acc_norm_stderr": 0.030176808288974337
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.3576158940397351,
"acc_stderr": 0.03913453431177258,
"acc_norm": 0.3576158940397351,
"acc_norm_stderr": 0.03913453431177258
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8201834862385321,
"acc_stderr": 0.016465345467391517,
"acc_norm": 0.8201834862385321,
"acc_norm_stderr": 0.016465345467391517
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.4861111111111111,
"acc_stderr": 0.03408655867977749,
"acc_norm": 0.4861111111111111,
"acc_norm_stderr": 0.03408655867977749
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8137254901960784,
"acc_stderr": 0.027325470966716312,
"acc_norm": 0.8137254901960784,
"acc_norm_stderr": 0.027325470966716312
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.7721518987341772,
"acc_stderr": 0.02730348459906943,
"acc_norm": 0.7721518987341772,
"acc_norm_stderr": 0.02730348459906943
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6905829596412556,
"acc_stderr": 0.03102441174057221,
"acc_norm": 0.6905829596412556,
"acc_norm_stderr": 0.03102441174057221
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.7404580152671756,
"acc_stderr": 0.03844876139785271,
"acc_norm": 0.7404580152671756,
"acc_norm_stderr": 0.03844876139785271
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.7768595041322314,
"acc_stderr": 0.03800754475228733,
"acc_norm": 0.7768595041322314,
"acc_norm_stderr": 0.03800754475228733
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7129629629629629,
"acc_stderr": 0.04373313040914761,
"acc_norm": 0.7129629629629629,
"acc_norm_stderr": 0.04373313040914761
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7607361963190185,
"acc_stderr": 0.0335195387952127,
"acc_norm": 0.7607361963190185,
"acc_norm_stderr": 0.0335195387952127
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.44642857142857145,
"acc_stderr": 0.04718471485219588,
"acc_norm": 0.44642857142857145,
"acc_norm_stderr": 0.04718471485219588
},
"harness|hendrycksTest-management|5": {
"acc": 0.7766990291262136,
"acc_stderr": 0.04123553189891431,
"acc_norm": 0.7766990291262136,
"acc_norm_stderr": 0.04123553189891431
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8504273504273504,
"acc_stderr": 0.023365051491753715,
"acc_norm": 0.8504273504273504,
"acc_norm_stderr": 0.023365051491753715
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.69,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.69,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8071519795657727,
"acc_stderr": 0.014108533515757431,
"acc_norm": 0.8071519795657727,
"acc_norm_stderr": 0.014108533515757431
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.6820809248554913,
"acc_stderr": 0.025070713719153186,
"acc_norm": 0.6820809248554913,
"acc_norm_stderr": 0.025070713719153186
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.4044692737430168,
"acc_stderr": 0.01641444091729315,
"acc_norm": 0.4044692737430168,
"acc_norm_stderr": 0.01641444091729315
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.7026143790849673,
"acc_stderr": 0.02617390850671858,
"acc_norm": 0.7026143790849673,
"acc_norm_stderr": 0.02617390850671858
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.684887459807074,
"acc_stderr": 0.02638527370346448,
"acc_norm": 0.684887459807074,
"acc_norm_stderr": 0.02638527370346448
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7191358024691358,
"acc_stderr": 0.02500646975579921,
"acc_norm": 0.7191358024691358,
"acc_norm_stderr": 0.02500646975579921
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.475177304964539,
"acc_stderr": 0.029790719243829714,
"acc_norm": 0.475177304964539,
"acc_norm_stderr": 0.029790719243829714
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.455019556714472,
"acc_stderr": 0.012718456618701766,
"acc_norm": 0.455019556714472,
"acc_norm_stderr": 0.012718456618701766
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.6286764705882353,
"acc_stderr": 0.029349803139765873,
"acc_norm": 0.6286764705882353,
"acc_norm_stderr": 0.029349803139765873
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6241830065359477,
"acc_stderr": 0.01959402113657744,
"acc_norm": 0.6241830065359477,
"acc_norm_stderr": 0.01959402113657744
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6454545454545455,
"acc_stderr": 0.045820048415054174,
"acc_norm": 0.6454545454545455,
"acc_norm_stderr": 0.045820048415054174
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.689795918367347,
"acc_stderr": 0.029613459872484375,
"acc_norm": 0.689795918367347,
"acc_norm_stderr": 0.029613459872484375
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8507462686567164,
"acc_stderr": 0.02519692987482707,
"acc_norm": 0.8507462686567164,
"acc_norm_stderr": 0.02519692987482707
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.86,
"acc_stderr": 0.034873508801977704,
"acc_norm": 0.86,
"acc_norm_stderr": 0.034873508801977704
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5120481927710844,
"acc_stderr": 0.03891364495835817,
"acc_norm": 0.5120481927710844,
"acc_norm_stderr": 0.03891364495835817
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8187134502923976,
"acc_stderr": 0.029547741687640044,
"acc_norm": 0.8187134502923976,
"acc_norm_stderr": 0.029547741687640044
},
"harness|truthfulqa:mc|0": {
"mc1": 0.5177478580171359,
"mc1_stderr": 0.017492470843075356,
"mc2": 0.6813898639090257,
"mc2_stderr": 0.014976157561060141
},
"harness|winogrande|5": {
"acc": 0.8113654301499605,
"acc_stderr": 0.0109951723180198
},
"harness|gsm8k|5": {
"acc": 0.5223654283548143,
"acc_stderr": 0.013758699485911838
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
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#### Who are the source data producers?
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### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
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[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
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#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
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Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
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## Dataset Card Contact
[More Information Needed] |
CVasNLPExperiments/VQAv2_sample_validation_google_flan_t5_xxl_mode_D_PNP_GENERIC_Q_rices_ns_1000 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: question
dtype: string
- name: true_label
sequence: string
- name: prediction
dtype: string
splits:
- name: fewshot_0_clip_tags_LAION_ViT_H_14_2B_with_openai_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_DETA_detections_deta_swin_large_o365_coco_classes_caption_all_patches_Salesforce_blip_image_captioning_large_clean_
num_bytes: 141736
num_examples: 1000
download_size: 53453
dataset_size: 141736
---
# Dataset Card for "VQAv2_sample_validation_google_flan_t5_xxl_mode_D_PNP_GENERIC_Q_rices_ns_1000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
mask-distilled-one-sec-cv12/chunk_24 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 895647156
num_examples: 175893
download_size: 912979643
dataset_size: 895647156
---
# Dataset Card for "chunk_24"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/cath_fireemblem | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of cath (Fire Emblem)
This is the dataset of cath (Fire Emblem), containing 33 images and their tags.
The core tags of this character are `breasts, brown_eyes, long_hair, orange_hair, brown_hair, ponytail`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:-----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 33 | 30.22 MiB | [Download](https://huggingface.co/datasets/CyberHarem/cath_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 33 | 19.29 MiB | [Download](https://huggingface.co/datasets/CyberHarem/cath_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 66 | 36.25 MiB | [Download](https://huggingface.co/datasets/CyberHarem/cath_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 33 | 27.48 MiB | [Download](https://huggingface.co/datasets/CyberHarem/cath_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 66 | 48.42 MiB | [Download](https://huggingface.co/datasets/CyberHarem/cath_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/cath_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 12 |  |  |  |  |  | 1girl, elbow_gloves, scarf, solo, fingerless_gloves, belt, smile, weapon |
| 1 | 5 |  |  |  |  |  | 1girl, green_scarf, solo, belt, blush, braided_ponytail, elbow_gloves, looking_at_viewer, parted_bangs, simple_background, white_background, bare_shoulders, fingerless_gloves, green_gloves, green_sleeves, large_breasts, open_mouth, pouch, shirt, :d, armpits, arms_up, cowboy_shot, jewelry, sleeveless_dress, upper_body, white_dress, yellow_eyes |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | elbow_gloves | scarf | solo | fingerless_gloves | belt | smile | weapon | green_scarf | blush | braided_ponytail | looking_at_viewer | parted_bangs | simple_background | white_background | bare_shoulders | green_gloves | green_sleeves | large_breasts | open_mouth | pouch | shirt | :d | armpits | arms_up | cowboy_shot | jewelry | sleeveless_dress | upper_body | white_dress | yellow_eyes |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------------|:--------|:-------|:--------------------|:-------|:--------|:---------|:--------------|:--------|:-------------------|:--------------------|:---------------|:--------------------|:-------------------|:-----------------|:---------------|:----------------|:----------------|:-------------|:--------|:--------|:-----|:----------|:----------|:--------------|:----------|:-------------------|:-------------|:--------------|:--------------|
| 0 | 12 |  |  |  |  |  | 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 | X | X | X | X | X | X | X |
|
CyberHarem/nowaki_kantaicollection | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of nowaki/野分/野分 (Kantai Collection)
This is the dataset of nowaki/野分/野分 (Kantai Collection), containing 433 images and their tags.
The core tags of this character are `grey_hair, asymmetrical_hair, grey_eyes, bangs, flipped_hair, swept_bangs, long_hair`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 433 | 308.51 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nowaki_kantaicollection/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 433 | 223.35 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nowaki_kantaicollection/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 871 | 440.39 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nowaki_kantaicollection/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 433 | 289.00 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nowaki_kantaicollection/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 871 | 549.52 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nowaki_kantaicollection/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/nowaki_kantaicollection',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 12 |  |  |  |  |  | 1girl, black_pantyhose, black_skirt, pleated_skirt, solo, white_background, white_shirt, yellow_necktie, black_vest, simple_background, dress_shirt, white_gloves, looking_at_viewer, school_uniform, standing, full_body |
| 1 | 15 |  |  |  |  |  | 1girl, pleated_skirt, school_uniform, shirt, solo, vest, white_gloves, yellow_necktie, black_pantyhose, machinery, simple_background, short_sleeves, white_background |
| 2 | 13 |  |  |  |  |  | 1girl, black_skirt, black_vest, dress_shirt, paw_gloves, pleated_skirt, solo, white_shirt, wolf_ears, wolf_tail, yellow_necktie, black_pantyhose, adapted_costume, simple_background, white_background, cowboy_shot, long_sleeves, looking_at_viewer, fake_animal_ears |
| 3 | 10 |  |  |  |  |  | 1girl, black_vest, short_sleeves, solo, upper_body, white_shirt, yellow_necktie, simple_background, looking_at_viewer, school_uniform, white_background, open_vest, white_gloves, hair_between_eyes |
| 4 | 6 |  |  |  |  |  | detached_collar, fake_animal_ears, playboy_bunny, rabbit_ears, 1girl, solo, yellow_necktie, black_leotard, black_pantyhose, cowboy_shot, simple_background, small_breasts, strapless_leotard, wrist_cuffs, covered_navel, looking_at_viewer, rabbit_tail, white_gloves |
| 5 | 7 |  |  |  |  |  | 1girl, blush, solo, looking_at_viewer, simple_background, underwear_only, white_background, black_bra, cat_cutout, cat_lingerie, cleavage_cutout, navel, black_panties, cat_ears, cat_tail, collarbone, cowboy_shot, frilled_bra, hair_between_eyes, small_breasts |
| 6 | 15 |  |  |  |  |  | 1girl, solo, yukata, obi, looking_at_viewer, white_kimono, upper_body, blush, wide_sleeves, holding, smile, alternate_costume, green_eyes, open_mouth, simple_background |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | black_pantyhose | black_skirt | pleated_skirt | solo | white_background | white_shirt | yellow_necktie | black_vest | simple_background | dress_shirt | white_gloves | looking_at_viewer | school_uniform | standing | full_body | shirt | vest | machinery | short_sleeves | paw_gloves | wolf_ears | wolf_tail | adapted_costume | cowboy_shot | long_sleeves | fake_animal_ears | upper_body | open_vest | hair_between_eyes | detached_collar | playboy_bunny | rabbit_ears | black_leotard | small_breasts | strapless_leotard | wrist_cuffs | covered_navel | rabbit_tail | blush | underwear_only | black_bra | cat_cutout | cat_lingerie | cleavage_cutout | navel | black_panties | cat_ears | cat_tail | collarbone | frilled_bra | yukata | obi | white_kimono | wide_sleeves | holding | smile | alternate_costume | green_eyes | open_mouth |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:------------------|:--------------|:----------------|:-------|:-------------------|:--------------|:-----------------|:-------------|:--------------------|:--------------|:---------------|:--------------------|:-----------------|:-----------|:------------|:--------|:-------|:------------|:----------------|:-------------|:------------|:------------|:------------------|:--------------|:---------------|:-------------------|:-------------|:------------|:--------------------|:------------------|:----------------|:--------------|:----------------|:----------------|:--------------------|:--------------|:----------------|:--------------|:--------|:-----------------|:------------|:-------------|:---------------|:------------------|:--------|:----------------|:-----------|:-----------|:-------------|:--------------|:---------|:------|:---------------|:---------------|:----------|:--------|:--------------------|:-------------|:-------------|
| 0 | 12 |  |  |  |  |  | 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 | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 13 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | | X | | | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 10 |  |  |  |  |  | X | | | | X | X | X | X | X | X | | X | X | X | | | | | | X | | | | | | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 6 |  |  |  |  |  | X | X | | | X | | | X | | X | | X | X | | | | | | | | | | | | X | | X | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | |
| 5 | 7 |  |  |  |  |  | X | | | | X | X | | | | X | | | X | | | | | | | | | | | | X | | | | | X | | | | | X | | | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | |
| 6 | 15 |  |  |  |  |  | X | | | | X | | | | | X | | | X | | | | | | | | | | | | | | | X | | | | | | | | | | | | X | | | | | | | | | | | | X | X | X | X | X | X | X | X | X |
|
pparshakov/grdmr_test_zoo_648292 | ---
license: mit
---
|
distilled-from-one-sec-cv12/chunk_171 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 1198599128
num_examples: 233554
download_size: 1211728971
dataset_size: 1198599128
---
# Dataset Card for "chunk_171"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
arianhosseini/openai_comparisons_20k_regen_and_relabelled | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
splits:
- name: train
num_bytes: 36118675
num_examples: 20000
- name: test
num_bytes: 8322345
num_examples: 5000
download_size: 21718388
dataset_size: 44441020
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
CyberHarem/shez_fireemblem | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of shez (Fire Emblem)
This is the dataset of shez (Fire Emblem), containing 393 images and their tags.
The core tags of this character are `purple_hair, hair_over_one_eye, purple_eyes, long_hair, breasts, bangs, hair_bun, large_breasts, 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 | 393 | 589.63 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shez_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 393 | 317.47 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shez_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 926 | 660.82 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shez_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 393 | 513.39 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shez_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 926 | 979.72 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shez_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/shez_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 24 |  |  |  |  |  | 1girl, armor, asymmetrical_clothes, cape, choker, cleavage, closed_mouth, gloves, looking_at_viewer, simple_background, smile, solo |
| 1 | 7 |  |  |  |  |  | 1girl, armor, asymmetrical_clothes, cape, choker, cleavage, closed_mouth, gloves, looking_at_viewer, simple_background, solo |
| 2 | 5 |  |  |  |  |  | 1girl, armor, asymmetrical_clothes, cape, choker, cleavage, closed_mouth, looking_at_viewer, simple_background, smile, solo |
| 3 | 10 |  |  |  |  |  | 1girl, armor, asymmetrical_clothes, cape, choker, cleavage, closed_mouth, looking_at_viewer, simple_background, solo |
| 4 | 14 |  |  |  |  |  | 1girl, armor, asymmetrical_clothes, cape, choker, cleavage, closed_mouth, looking_at_viewer, simple_background, solo, sword, gloves, holding |
| 5 | 6 |  |  |  |  |  | 1girl, armor, asymmetrical_clothes, cape, choker, cleavage, closed_mouth, dual_wielding, looking_at_viewer, simple_background, solo, gloves, holding_sword |
| 6 | 5 |  |  |  |  |  | 2girls, asymmetrical_clothes, cape, choker, cleavage, closed_mouth, simple_background, armor, gloves, medium_hair, smile, looking_at_viewer, closed_eyes |
| 7 | 5 |  |  |  |  |  | 1girl, black_bikini, choker, cleavage, hair_flower, navel, official_alternate_costume, smile, solo, closed_mouth, looking_at_viewer, simple_background, bare_shoulders, white_background |
| 8 | 6 |  |  |  |  |  | 1girl, black_bikini, choker, cleavage, fingerless_gloves, hair_flower, looking_at_viewer, navel, official_alternate_costume, solo, smile |
| 9 | 11 |  |  |  |  |  | 1girl, hetero, nipples, penis, choker, sex, 1boy, armor, asymmetrical_clothes, blush, cum_in_pussy, spread_legs, vaginal, looking_at_viewer, mosaic_censoring, gloves, rape, breasts_out, cleavage, cum_on_breasts, cum_on_hair, facial, navel, open_mouth, thighhighs |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | armor | asymmetrical_clothes | cape | choker | cleavage | closed_mouth | gloves | looking_at_viewer | simple_background | smile | solo | sword | holding | dual_wielding | holding_sword | 2girls | medium_hair | closed_eyes | black_bikini | hair_flower | navel | official_alternate_costume | bare_shoulders | white_background | fingerless_gloves | hetero | nipples | penis | sex | 1boy | blush | cum_in_pussy | spread_legs | vaginal | mosaic_censoring | rape | breasts_out | cum_on_breasts | cum_on_hair | facial | open_mouth | thighhighs |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:-----------------------|:-------|:---------|:-----------|:---------------|:---------|:--------------------|:--------------------|:--------|:-------|:--------|:----------|:----------------|:----------------|:---------|:--------------|:--------------|:---------------|:--------------|:--------|:-----------------------------|:-----------------|:-------------------|:--------------------|:---------|:----------|:--------|:------|:-------|:--------|:---------------|:--------------|:----------|:-------------------|:-------|:--------------|:-----------------|:--------------|:---------|:-------------|:-------------|
| 0 | 24 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 7 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 5 |  |  |  |  |  | X | X | X | X | X | X | X | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 10 |  |  |  |  |  | X | X | X | X | X | X | X | | X | X | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 14 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 6 |  |  |  |  |  | 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 | | | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 5 |  |  |  |  |  | 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 | | | | | | | | | | | | | | | | | |
| 9 | 11 |  |  |  |  |  | X | X | X | | X | X | | X | X | | | | | | | | | | | | | X | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
ctoraman/large-scale-hate-speech-turkish-v2 | ---
license: cc-by-nc-sa-4.0
task_categories:
- text-classification
language:
- tr
tags:
- hate speech
- hate speech detection
- hate-speech
- tweets
- social media
- topic
- hate-speech-detection
---
The dataset published in the LREC 2022 paper "Large-Scale Hate Speech Detection with Cross-Domain Transfer".
# This is Dataset v2 (Turkish):
The modified dataset that includes 60,310 tweets in Turkish. The annotations with more than 80% agreement are included.
TweetID: Tweet ID from Twitter API
LangID: 0 (Turkish)
TopicID: Domain of the topic 0-Religion, 1-Gender, 2-Race, 3-Politics, 4-Sports
HateLabel: Final hate label decision 0-Normal, 1-Offensive, 2-Hate
# GitHub Repo:
https://github.com/avaapm/hatespeech
# Citation:
Toraman, C., Şahinuç, F., & Yilmaz, E. (2022, June). Large-Scale Hate Speech Detection with Cross-Domain Transfer. In Proceedings of the Thirteenth Language Resources and Evaluation Conference (pp. 2215-2225). |
alexandrainst/ddisco | ---
annotations_creators:
- expert-generated
language:
- da
language_creators:
- expert-generated
license:
- afl-3.0
multilinguality:
- monolingual
pretty_name: DDisco
size_categories:
- 1K<n<10K
source_datasets: []
tags:
- discourse
- coherence
task_categories:
- text-classification
task_ids: []
dataset_info:
features:
- name: text
dtype: string
- name: domain
dtype: string
- name: rating
dtype: int64
splits:
- name: train
num_bytes: 815571
num_examples: 801
- name: test
num_bytes: 209297
num_examples: 201
download_size: 672202
dataset_size: 1024868
---
# Dataset Card for DDisco
## Dataset Description
The DDisco dataset is a dataset which can be used to train models to classify levels of coherence in _danish_ discourse. Each entry in the dataset is annotated with a discourse coherence label (rating from 1 to 3):
1: low coherence (difficult to understand, unorganized, contained unnecessary details and can not be summarized briefly and easily)
2: medium coherence
3: high coherence (easy to understand, well organized, only contain details that support the main point and can be summarized briefly and easily).
Grammatical and typing errors are ignored (i.e. they do not affect the coherency score) and the coherence of a text is considered within its own domain.
### Additional Information
[DDisCo: A Discourse Coherence Dataset for Danish](https://aclanthology.org/2022.lrec-1.260.pdf)
### Contributions
[@ajders](https://github.com/ajders) |
blockplacer4/hobby-dataset | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: Input
dtype: string
- name: Output
dtype: string
- name: Text
dtype: string
splits:
- name: train
num_bytes: 217380
num_examples: 512
download_size: 39563
dataset_size: 217380
---
annotations_creators:
- expert-generated
language:
- de
language_creators:
- expert-generated
- machine-generated
license:
- mit
multilinguality:
- monolingual
paperswithcode_id: acronym-identification
pretty_name: Hobby-KI
size_categories:
- n<1K
source_datasets:
- original
tags: []
task_categories:
- text-generation
task_ids:
- dialogue-modeling
train-eval-index:
- col_mapping:
labels: tags
tokens: tokens
config: default
splits:
eval_split: test
task: token-classification
task_id: entity_extraction |
massi/capitals | ---
license: cc-by-nc-sa-4.0
---
|
Weni/Zeroshot_Train-20K_bias_tweet-format | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: source_text
dtype: string
- name: target_text
dtype: string
splits:
- name: train
num_bytes: 4338493
num_examples: 20000
download_size: 1744022
dataset_size: 4338493
task_categories:
- zero-shot-classification
language:
- pt
size_categories:
- 10K<n<100K
---
# Dataset Card for "Zeroshot_Train-20K_bias_tweet-format"
This dataset is a train dataset for the Zeroshot models.
It has 20.000 data in a prompt format exclusively for train with class 'bias' in Brazilian Portuguese.
Prompt:
```
"Classifique o tweet entre 'classe1', 'classe2', 'classe3', 'classe4', 'bias' \\n\\nTweet: frase \\n\\nLabel: 'other'
```
The dataset was divided as follows: <br>
```
- 6,000 data: prompt with class option without target class (bias)
- 7,000 data: prompt with class option + target class included as an option. target class is not correct
- 7,000 data: prompt with class option + target class. target class is correct
```
## How to load and use this dataset:
```
from datasets import load_dataset
dataset = load_dataset("Weni/Zeroshot_Train-20K_bias_tweet-format")
dataset
```
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
jay401521/label2 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: id
dtype: int64
- name: domain
dtype: string
- name: label
dtype: int64
- name: rank
dtype: int64
- name: sentence
dtype: string
splits:
- name: train
num_bytes: 922790.3333333334
num_examples: 10007
download_size: 463496
dataset_size: 922790.3333333334
---
# Dataset Card for "label2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Sijuade/cifar64-latents | ---
license: mit
dataset_info:
features:
- name: latent
sequence:
sequence:
sequence: float32
- name: noised_latents
sequence:
sequence:
sequence: float32
- name: noise
sequence:
sequence:
sequence:
sequence: float32
- name: timesteps
dtype: float64
- name: label
dtype: int64
splits:
- name: train
num_bytes: 212160000
num_examples: 60000
download_size: 288948028
dataset_size: 212160000
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
open-llm-leaderboard/details_maywell__Synatra-V0.1-7B-Instruct | ---
pretty_name: Evaluation run of maywell/Synatra-V0.1-7B-Instruct
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [maywell/Synatra-V0.1-7B-Instruct](https://huggingface.co/maywell/Synatra-V0.1-7B-Instruct)\
\ 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\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_maywell__Synatra-V0.1-7B-Instruct_public\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-11-06T18:05:12.244898](https://huggingface.co/datasets/open-llm-leaderboard/details_maywell__Synatra-V0.1-7B-Instruct_public/blob/main/results_2023-11-06T18-05-12.244898.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\": 0.32246224832214765,\n\
\ \"em_stderr\": 0.004786806140711669,\n \"f1\": 0.3963055788590608,\n\
\ \"f1_stderr\": 0.004634063813539812,\n \"acc\": 0.46089483255174657,\n\
\ \"acc_stderr\": 0.011702308149823175\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.32246224832214765,\n \"em_stderr\": 0.004786806140711669,\n\
\ \"f1\": 0.3963055788590608,\n \"f1_stderr\": 0.004634063813539812\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.19408642911296436,\n \
\ \"acc_stderr\": 0.010893918308192417\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7277032359905288,\n \"acc_stderr\": 0.012510697991453932\n\
\ }\n}\n```"
repo_url: https://huggingface.co/maywell/Synatra-V0.1-7B-Instruct
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_11_06T18_05_12.244898
path:
- '**/details_harness|drop|3_2023-11-06T18-05-12.244898.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-11-06T18-05-12.244898.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_11_06T18_05_12.244898
path:
- '**/details_harness|gsm8k|5_2023-11-06T18-05-12.244898.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-11-06T18-05-12.244898.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_11_06T18_05_12.244898
path:
- '**/details_harness|winogrande|5_2023-11-06T18-05-12.244898.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-11-06T18-05-12.244898.parquet'
- config_name: results
data_files:
- split: 2023_11_06T18_05_12.244898
path:
- results_2023-11-06T18-05-12.244898.parquet
- split: latest
path:
- results_2023-11-06T18-05-12.244898.parquet
---
# Dataset Card for Evaluation run of maywell/Synatra-V0.1-7B-Instruct
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/maywell/Synatra-V0.1-7B-Instruct
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [maywell/Synatra-V0.1-7B-Instruct](https://huggingface.co/maywell/Synatra-V0.1-7B-Instruct) 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_maywell__Synatra-V0.1-7B-Instruct_public",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-11-06T18:05:12.244898](https://huggingface.co/datasets/open-llm-leaderboard/details_maywell__Synatra-V0.1-7B-Instruct_public/blob/main/results_2023-11-06T18-05-12.244898.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": 0.32246224832214765,
"em_stderr": 0.004786806140711669,
"f1": 0.3963055788590608,
"f1_stderr": 0.004634063813539812,
"acc": 0.46089483255174657,
"acc_stderr": 0.011702308149823175
},
"harness|drop|3": {
"em": 0.32246224832214765,
"em_stderr": 0.004786806140711669,
"f1": 0.3963055788590608,
"f1_stderr": 0.004634063813539812
},
"harness|gsm8k|5": {
"acc": 0.19408642911296436,
"acc_stderr": 0.010893918308192417
},
"harness|winogrande|5": {
"acc": 0.7277032359905288,
"acc_stderr": 0.012510697991453932
}
}
```
### 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] |
citiusLTL/Twitter-COVID-19 | ---
license: gpl-3.0
task_categories:
- text-classification
language:
- es
- en
---
**General description**:
This dataset comprisses a set of tweets crawled during the COVID-19 pandemic (from March 2020 to June 2021). Tweets are located in two different regions: Spain and USA. This adds value to the collection, as it contains data in two languages.
This data was used as part of a broader study that aimed to determine the evolution of different personality traits and disorders during the pandemic. Thus, weak labels for different dimensions, such as sentiment, personality prevalence, and others, are also available.
Further details about this experimentation can be found in the [paper](https://link.springer.com/article/10.1007/s10844-023-00810-3) or [Github](https://github.com/MarcosFP97/COVID-19-Personality).
**Data**:
A sample of the data can be visualised and downloaded from this card. More specifically, it corresponds to the month of January 2021 and tweets are located on USA. Tweets were anonymized for privacy reasons.
The whole dataset is available upon request to fullfil Twitter's restrictions. You can contact either with marcosfernandez.pichel@usc.es or ezra.aragon@usc.es to obtain it.
**Citation**:
For all the future studies using our data, we kindly ask to quote our paper:
@article{fernandez2023personality, \
title={Personality trait analysis during the COVID-19 pandemic: a comparative study on social media}, \
author={Fern{\'a}ndez-Pichel, Marcos and Arag{\'o}n, Mario Ezra and Saborido-Pati{\~n}o, Juli{\'a}n and Losada, David E}, \
journal={Journal of Intelligent Information Systems}, \
pages={1--26}, \
year={2023}, \
publisher={Springer} \
}
|
Seanxh/twitter_dataset_1713216897 | ---
dataset_info:
features:
- name: id
dtype: string
- name: tweet_content
dtype: string
- name: user_name
dtype: string
- name: user_id
dtype: string
- name: created_at
dtype: string
- name: url
dtype: string
- name: favourite_count
dtype: int64
- name: scraped_at
dtype: string
- name: image_urls
dtype: string
splits:
- name: train
num_bytes: 194829
num_examples: 456
download_size: 67581
dataset_size: 194829
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Digote/Oliver | ---
license: openrail
---
|
Marcis/Cleitin | ---
license: apache-2.0
---
|
shiva33/autotrain-data-finetuning | ---
language:
- en
task_categories:
- summarization
---
# AutoTrain Dataset for project: finetuning
## Dataset Description
This dataset has been automatically processed by AutoTrain for project finetuning.
### Languages
The BCP-47 code for the dataset's language is en.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"feat_Chapter": "Chapter IV",
"text": "78",
"feat_Description": "Act done pursuant to the judgment or order of the court.",
"target": "Nothing which is done in pursuance of, or which is warranted by the judgment or order of, a Court of Justice, if done whilst such judgment or order remains in force, is an offence, notwithstanding the Court may have had no jurisdiction to pass such judgment or order, provided the person doing the act in good faith believes that the Court had such jurisdiction.",
"feat_Unnamed: 4": null,
"feat_Unnamed: 5": null
},
{
"feat_Chapter": "Chapter 16",
"text": "SECTION 341",
"feat_Description": "Punishment for wrongful restraint",
"target": "This section specifies the punishment for wrongful restraint. The penalty varies depending on the degree of restraint and the circumstances surrounding the offense.",
"feat_Unnamed: 4": null,
"feat_Unnamed: 5": null
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"feat_Chapter": "Value(dtype='string', id=None)",
"text": "Value(dtype='string', id=None)",
"feat_Description": "Value(dtype='string', id=None)",
"target": "Value(dtype='string', id=None)",
"feat_Unnamed: 4": "Value(dtype='string', id=None)",
"feat_Unnamed: 5": "Value(dtype='string', id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 389 |
| valid | 98 |
|
one-sec-cv12/chunk_192 | ---
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
splits:
- name: train
num_bytes: 25040481984.5
num_examples: 260708
download_size: 21707886715
dataset_size: 25040481984.5
---
# Dataset Card for "chunk_192"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Falah/ballet_dancing_style_art_prompts | ---
dataset_info:
features:
- name: prompts
dtype: string
splits:
- name: train
num_bytes: 29462190
num_examples: 100000
download_size: 3160092
dataset_size: 29462190
---
# Dataset Card for "ballet_dancing_style_art_prompts"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
JonasWeinert/in-intdev-jd | ---
task_categories:
- zero-shot-classification
language:
- en
pretty_name: skills in nternational development job descriptions
size_categories:
- 1K<n<10K
--- |
rezanayebi/Data0 | ---
license: apache-2.0
---
"The First Real Estate Pre-Sale System in Tehran"
The "First Real Estate Pre-Sale System in Tehran" represents a significant innovation in the real estate industry, offering remarkable benefits to individuals and prospective property buyers in Tehran. This system acts as a bridge between sellers and buyers, providing easy access to properties of interest.
This system alleviates common concerns and challenges associated with the property-buying process, such as finding accurate and up-to-date information, estimating fair prices, and negotiating deals. Some key features of this system include:
1. **Precise Search**: The ability to search for properties based on criteria such as location, property type, price, size, and other specifications, allowing you to quickly find your desired property.
2. **Comprehensive Property Information**: Detailed and comprehensive property information, including photos, technical specifications, descriptions, and property maps, is provided.
3. **Negotiations and Consultations**: The system enables direct communication with sellers to negotiate prices and deal terms. Additionally, real estate consultants are available to help you make informed decisions about property purchases.
4. **Property Pre-Sale**: You have the opportunity to consider properties that have not yet been released to the market and benefit from fair prices during the pre-sale phase.
5. **Comparison of Options**: You can compare various options and make the best choice for your needs.
6. **Customer-Centric Services**: The system offers post-sale services, legal consultation, and credit facilities.
7. https://www.tehran-borj.ir
The "First Real Estate Pre-Sale System in Tehran" allows you to navigate the property market with ease and confidence, making it one of the prominent examples of innovation in the real estate industry. |
EinsZwo/nlid_10k | ---
dataset_info:
features:
- name: lang
dtype: string
- name: doc
dtype: string
- name: supertags
dtype: string
- name: supertag_list
sequence: string
- name: nlid_label
dtype: int64
splits:
- name: train
num_bytes: 867577975
num_examples: 140250
- name: test
num_bytes: 82888310
num_examples: 13524
- name: dev
num_bytes: 31183666
num_examples: 13526
download_size: 125544588
dataset_size: 981649951
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: dev
path: data/dev-*
---
|
BangumiBase/overlord | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Overlord
This is the image base of bangumi OVERLORD, we detected 65 characters, 4389 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 | 76 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 27 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 354 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 117 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 48 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 89 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 32 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 60 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 64 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 17 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
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| 22 | 55 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
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| 32 | 51 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 6 | [Download](33/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
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| 43 | 53 | [Download](43/dataset.zip) |  |  |  |  |  |  |  |  |
| 44 | 6 | [Download](44/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
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| 55 | 27 | [Download](55/dataset.zip) |  |  |  |  |  |  |  |  |
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| 57 | 13 | [Download](57/dataset.zip) |  |  |  |  |  |  |  |  |
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| 60 | 35 | [Download](60/dataset.zip) |  |  |  |  |  |  |  |  |
| 61 | 43 | [Download](61/dataset.zip) |  |  |  |  |  |  |  |  |
| 62 | 51 | [Download](62/dataset.zip) |  |  |  |  |  |  |  |  |
| 63 | 24 | [Download](63/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 185 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
LevMuchnik/SupremeCourtOfIsrael | ---
license: openrail
language:
- he
tags:
- legal, verdicts, metadata, hebrew
pretty_name: Supreme Court Israel - Public Verdicts and Decisions
size_categories:
- 100K<n<1M
task_ids:
- language-modeling
- masked-language-modeling
- document-retrieval
task_categories:
- text-generation
- fill-mask
- text-retrieval
---
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
Lev Muchnik, lev.muchnik@mail.huji.ac.il
### Dataset Summary
This dataset represents a 2022 snapshot of the Supreme Court of Israel public verdicts and decisions supported by rich metadata. The 5.31GB dataset represents 751,194 documents.
Overall, the dataset contains 2.68 Gb of text.
It can be loaded with the dataset package:
```
import datasets
data = datasets.load_dataset('LevMuchnik/SupremeCourtOfIsrael')
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The vast majority of the documents in the database are in Hebrew. A small number of documents are in English.
## Dataset Structure
The dataset is a json lines file with each line corresponding to a single document and containing document identification, text and metadata.
### Data Instances
[More Information Needed]
### Data Fields
The file contains the following fields:
- case_id - running number for cases
- download_time - when the document was downloaded (datetime)
- number_of_case_documents - number of documents in the current case
- file_name - full name of the document file, including relative path
- Id - document id
- CaseId - case id
- VerdictDt - Date of the document (datetime)
- CreatedDate - Date of when the document was inserted into the Supreme Court database
- CaseNum - case number
- CaseDesc - Unique case identifier. This id is used to reference cases within the Israeli legal system
- Pages - number of pages in the original document
- Path - relative path to the document
- CaseName - formal name of the case
- FileName - document file name, without path
- DocName -document file name, without path
- Year - document creation year
- TypeCode - enumeration of document types (see Type field below)
- Type - Document type
- פסק-דין 84339
- החלטה 663099
- צו ביניים 22
- פסקי דין באנגלית 310
- צו על תנאי 200
- צו 2606
- פד"י 302
- תקצירים 316
- Technical - boolean indicator of whether the document is technical or not.
- CodeVolume - ?
- document_hash - 258-bit hashtag of the document name. Used internally to uniquely identify the document
- text - text of the document. Multiple newlines and other document formating elements (paragraphs,lists, etc.) are preserved.
- html_title - document title extracted from the HTML
- VerdictsDt - date of the verdict
- meta_case_nm - formal case name,
- meta_sec_appeal - integer or None
- meta_side_ty - case type, list of strings
- meta_verdict_file_nm - name of the verdict file
- meta_judge - list of names of the cases judges
- meta_mador_nm - name of the court instance (e.g. בג"ץ)
- meta_side_nm - list of the case parties, list of strings
- meta_verdict_dt - date of the verdict
- meta_case_dt - date of the case
- meta_verdict_nbr -
- meta_ProgId - name of the software used to create the document (None, Word, etc)
- meta_is_technical - whether the document is technical, {'false', 'true'}
- meta_judge_nm_last - last names of the judges (list of strings)
- meta_case_nbr - formal number of the case (same as CaseDesc)
- meta_verdict_ty - type of the decision (same as Type)
- meta_lawyer_nm - list of lawyer names, list of strings or None
- meta_judge_nm_first - list of judges' first names, list of strings
- meta_verdict_pages - number of document cases
- meta_inyan_nm - court בג"ץ
- meta_court_nm - court (e.g. בית המשפט העליון )
### Data Splits
The entire dataset is qualified as 'train'.
## Dataset Creation
2023-04-22
### Curation Rationale
[More Information Needed]
### Source Data
https://supreme.court.gov.il/
#### Initial Data Collection and Normalization
The data was colleted by crawling the Israeli Supreme Court website.
#### 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
The data contained in this dataset is public.
## 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
Prof. Lev Muchnik, Hebrew University of Jerusalem
Dr. Inbal Yahav Shenberger, Tel Aviv University
### Licensing Information
[More Information Needed]
### Citation Information
Lev Muchnik, Inbal Yahav, Ariel Nevo, Avichay Chriqui, Tim Shektov, 2023, The Israeli Supreme Court Dataset
### Contributions
The authours would like to thank the Israeli Innovation Authority (grants #78560 and #78561) for their support in creating of this dataset. |
irodkin/multiview_panohead | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
- name: view_45
dtype: image
- name: view_90
dtype: image
- name: view_180
dtype: image
- name: view_270
dtype: image
- name: view_above
dtype: image
splits:
- name: train
num_bytes: 3000408300.0
num_examples: 5000
download_size: 2997397205
dataset_size: 3000408300.0
---
# Dataset Card for "multiview_panohead"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
biki96/hf-stack-v1 | ---
dataset_info:
features:
- name: repo_id
dtype: string
- name: file_path
dtype: string
- name: content
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 117582122
num_examples: 7362
download_size: 39848159
dataset_size: 117582122
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
minorproj/custom-data | ---
license: apache-2.0
---
|
kimnt93/vi-seed-task-cls | ---
size_categories:
- n<1K
dataset_info:
features:
- name: task
dtype: string
- name: label_text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 3878
num_examples: 31
download_size: 5426
dataset_size: 3878
---
|
Llamas-competition/public_labeled_data | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': alpaca
'1': guanaco
'2': llama
'3': vicuna
- name: id
dtype: int64
splits:
- name: train
num_bytes: 9037914.166666666
num_examples: 287
download_size: 8628088
dataset_size: 9037914.166666666
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
kaleemWaheed/twitter_dataset_1713027367 | ---
dataset_info:
features:
- name: id
dtype: string
- name: tweet_content
dtype: string
- name: user_name
dtype: string
- name: user_id
dtype: string
- name: created_at
dtype: string
- name: url
dtype: string
- name: favourite_count
dtype: int64
- name: scraped_at
dtype: string
- name: image_urls
dtype: string
splits:
- name: train
num_bytes: 11791
num_examples: 28
download_size: 9645
dataset_size: 11791
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
DataStudio/OCR_underline_part_2 | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 1059849798.75
num_examples: 63410
download_size: 1060819033
dataset_size: 1059849798.75
---
# Dataset Card for "OCR_underline_part_2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
predict-SIREN-PSNR/COIN-collection | ---
license: mit
---
# COIN collection dataset (**👷 Under Construction 👷**)
This is the dataset for our paper "Predicting the Encoding Error of Implicit Neural Representations", currently under anonymous review. It consists of 300,000 small SIREN networks trained to encode square images from the MSCOCO dataset.
We will publish a loading script for this dataset soon, but until then, see the following instructions:
## How to Use
First, download this repository using:
`huggingface-cli repo download predict-SIREN-PSNR/COIN-collection --repo_type datasets`
There are two types of files in this dataset:
1. `.json.gz` files containing data about the SIRENs we have trained,
2. the images from the MSCOCO dataset that those SIRENs are trained on.
### MSCOCO images
To download the MSCOCO images:
1. `pip install img2dataset`
2. `cd data/mscoco`
3. `bash download_mscoco.sh`
This will download around 80Gb of images in `data/mscoco/mscoco`.
### SIREN run records
The sirens are organized into two sub-datasets, `single-architecture` and `many-architecture`. Each `.json.gz` file contains one SIREN per line, which can be loaded as a JSON object. Each SIREN record contains the following fields:
- `config`: The starting configuration of the SIREN training run. The `image_id` corresponds to the filename of the corresponding MSCOCO image. The `image_size` indicates what size the image was downsampled to, using PIL's `resize()` command with `BOX` resampling. The other arguments in `config` specify the arguments to be used in the [COIN](https://github.com/EmilienDupont/coin) training script to reproduce this SIREN run.
- `psnr_history`: record of the PSNR curve during training time.
- `best_psnr_history`: Similar to `psnr_history`, but stores the maximum value of `psnr history` seen up until this point during training.
- `iteration_history`: Parallel to the psnr_history and best_psnr_history; the training iteration at wich those PSNRs are recorded.
- `hp_bpp`: bits per pixel of the SIREN encoding of the image, with weights stored at half-precision (16-bit floats)
- `fp_bpp`: bits per pixel of the SIREN encoding of the image, with weights stored at full-precision (32-bit floats)
- `fp_psnr`: PSNR of the SIREN-based image reconstruction.
- `best_model`: Binary blob of the SIREN's `state_dict`.
|
joseluhf11/clinical_case_symptoms_diseases_dataset | ---
license: apache-2.0
---
|
Lancelot53/srbd1_v2_annotated_segmented | ---
dataset_info:
features:
- name: html
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 1623614
num_examples: 2434
download_size: 525557
dataset_size: 1623614
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "srbd1_v2_annotated_segmented"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
fast-flash/fast-flash-hackernews-users | ---
license: apache-2.0
tags:
- hackernews
- text
- social
- nlp
size_categories:
- 100K<n<1M
language:
- en
---
# Fast Flash | HackerNews Users Dataset
### Exploratory Analysis
Take a look at some fascinating findings from this dataset [on our website](http://wearefastflash.com/blog/hackernews).
### Dataset Summary
We release dataset of all HackerNews users who have posted at least once.
The dataset includes 853,840 users and was collected on Sunday, March 26, 2023.
You can find a dataset of all posts [right here](https://huggingface.co/datasets/fast-flash/fast-flash-hackernews-posts).
### Dataset Structure
The user objects in this dataset are structured according to HackerNews' [API specification](https://github.com/HackerNews/API).
## About the Author
[Fast Flash](https://wearefastflash.com) is a multidisciplinary creative studio that specializes in data-driven development, product design, branding, and tech.
Need help with design, coding, machine learning, pitch decks, data, or analytics?
Drop us a line at [hi@wearefastflash.com](mailto:hi@wearefastflash.com). |
open-llm-leaderboard/details_NousResearch__CodeLlama-13b-hf | ---
pretty_name: Evaluation run of NousResearch/CodeLlama-13b-hf
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [NousResearch/CodeLlama-13b-hf](https://huggingface.co/NousResearch/CodeLlama-13b-hf)\
\ 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\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_NousResearch__CodeLlama-13b-hf\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-18T22:40:09.407812](https://huggingface.co/datasets/open-llm-leaderboard/details_NousResearch__CodeLlama-13b-hf/blob/main/results_2023-10-18T22-40-09.407812.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\": 0.00033145814652192065,\n \"f1\": 0.05248531879194655,\n\
\ \"f1_stderr\": 0.0012515405190332619,\n \"acc\": 0.3964846847094825,\n\
\ \"acc_stderr\": 0.011095593973496732\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0010486577181208054,\n \"em_stderr\": 0.00033145814652192065,\n\
\ \"f1\": 0.05248531879194655,\n \"f1_stderr\": 0.0012515405190332619\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.12130401819560273,\n \
\ \"acc_stderr\": 0.008992888497275572\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.6716653512233622,\n \"acc_stderr\": 0.01319829944971789\n\
\ }\n}\n```"
repo_url: https://huggingface.co/NousResearch/CodeLlama-13b-hf
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_08_29T18_13_52.290314
path:
- '**/details_harness|arc:challenge|25_2023-08-29T18:13:52.290314.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-29T18:13:52.290314.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_18T22_40_09.407812
path:
- '**/details_harness|drop|3_2023-10-18T22-40-09.407812.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-18T22-40-09.407812.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_18T22_40_09.407812
path:
- '**/details_harness|gsm8k|5_2023-10-18T22-40-09.407812.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-18T22-40-09.407812.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_29T18_13_52.290314
path:
- '**/details_harness|hellaswag|10_2023-08-29T18:13:52.290314.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-29T18:13:52.290314.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_29T18_13_52.290314
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-29T18:13:52.290314.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-29T18:13:52.290314.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-29T18:13:52.290314.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_29T18_13_52.290314
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-29T18:13:52.290314.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-29T18:13:52.290314.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_29T18_13_52.290314
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-29T18:13:52.290314.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-29T18:13:52.290314.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_29T18_13_52.290314
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-29T18:13:52.290314.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-29T18:13:52.290314.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_29T18_13_52.290314
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-29T18:13:52.290314.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-29T18:13:52.290314.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_29T18_13_52.290314
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-29T18:13:52.290314.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-29T18:13:52.290314.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_29T18_13_52.290314
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-29T18:13:52.290314.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-29T18:13:52.290314.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_29T18_13_52.290314
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-29T18:13:52.290314.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-29T18:13:52.290314.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_29T18_13_52.290314
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-29T18:13:52.290314.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-29T18:13:52.290314.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_29T18_13_52.290314
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-29T18:13:52.290314.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-29T18:13:52.290314.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_29T18_13_52.290314
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-29T18:13:52.290314.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-29T18:13:52.290314.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_29T18_13_52.290314
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-29T18:13:52.290314.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-29T18:13:52.290314.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_29T18_13_52.290314
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-29T18:13:52.290314.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-29T18:13:52.290314.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_29T18_13_52.290314
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-29T18:13:52.290314.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-29T18:13:52.290314.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_29T18_13_52.290314
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-29T18:13:52.290314.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-29T18:13:52.290314.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_29T18_13_52.290314
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-29T18:13:52.290314.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-29T18:13:52.290314.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_29T18_13_52.290314
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-29T18:13:52.290314.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-29T18:13:52.290314.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_29T18_13_52.290314
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-29T18:13:52.290314.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-29T18:13:52.290314.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_29T18_13_52.290314
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-29T18:13:52.290314.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-29T18:13:52.290314.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_29T18_13_52.290314
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-29T18:13:52.290314.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-29T18:13:52.290314.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_29T18_13_52.290314
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-29T18:13:52.290314.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-29T18:13:52.290314.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_29T18_13_52.290314
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-29T18:13:52.290314.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-29T18:13:52.290314.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_29T18_13_52.290314
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-29T18:13:52.290314.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-29T18:13:52.290314.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_29T18_13_52.290314
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-29T18:13:52.290314.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-29T18:13:52.290314.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_29T18_13_52.290314
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-29T18:13:52.290314.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-29T18:13:52.290314.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_29T18_13_52.290314
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-29T18:13:52.290314.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-29T18:13:52.290314.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_08_29T18_13_52.290314
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-29T18:13:52.290314.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-29T18:13:52.290314.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_29T18_13_52.290314
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-29T18:13:52.290314.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-29T18:13:52.290314.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_08_29T18_13_52.290314
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-29T18:13:52.290314.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-29T18:13:52.290314.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_29T18_13_52.290314
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-29T18:13:52.290314.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-29T18:13:52.290314.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_29T18_13_52.290314
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-29T18:13:52.290314.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-29T18:13:52.290314.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_29T18_13_52.290314
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-29T18:13:52.290314.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-29T18:13:52.290314.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_29T18_13_52.290314
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-29T18:13:52.290314.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-29T18:13:52.290314.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_29T18_13_52.290314
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-29T18:13:52.290314.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-29T18:13:52.290314.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_29T18_13_52.290314
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-29T18:13:52.290314.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-29T18:13:52.290314.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_29T18_13_52.290314
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-29T18:13:52.290314.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-29T18:13:52.290314.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_29T18_13_52.290314
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-29T18:13:52.290314.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-29T18:13:52.290314.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_29T18_13_52.290314
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-29T18:13:52.290314.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-29T18:13:52.290314.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_08_29T18_13_52.290314
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-29T18:13:52.290314.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-29T18:13:52.290314.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_08_29T18_13_52.290314
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-29T18:13:52.290314.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-29T18:13:52.290314.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_29T18_13_52.290314
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-29T18:13:52.290314.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-29T18:13:52.290314.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_29T18_13_52.290314
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-29T18:13:52.290314.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-29T18:13:52.290314.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_29T18_13_52.290314
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-29T18:13:52.290314.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-29T18:13:52.290314.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_29T18_13_52.290314
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-29T18:13:52.290314.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-29T18:13:52.290314.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_29T18_13_52.290314
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-29T18:13:52.290314.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-29T18:13:52.290314.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_29T18_13_52.290314
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-29T18:13:52.290314.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-29T18:13:52.290314.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_29T18_13_52.290314
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-29T18:13:52.290314.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-29T18:13:52.290314.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_29T18_13_52.290314
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-29T18:13:52.290314.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-29T18:13:52.290314.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_29T18_13_52.290314
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-29T18:13:52.290314.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-29T18:13:52.290314.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_29T18_13_52.290314
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-29T18:13:52.290314.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-29T18:13:52.290314.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_29T18_13_52.290314
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-29T18:13:52.290314.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-29T18:13:52.290314.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_29T18_13_52.290314
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-29T18:13:52.290314.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-29T18:13:52.290314.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_29T18_13_52.290314
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-29T18:13:52.290314.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-29T18:13:52.290314.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_29T18_13_52.290314
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-29T18:13:52.290314.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-29T18:13:52.290314.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_29T18_13_52.290314
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-29T18:13:52.290314.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-29T18:13:52.290314.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_29T18_13_52.290314
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-29T18:13:52.290314.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-29T18:13:52.290314.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_29T18_13_52.290314
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-29T18:13:52.290314.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-29T18:13:52.290314.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_29T18_13_52.290314
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-29T18:13:52.290314.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-29T18:13:52.290314.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_29T18_13_52.290314
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-29T18:13:52.290314.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-29T18:13:52.290314.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_18T22_40_09.407812
path:
- '**/details_harness|winogrande|5_2023-10-18T22-40-09.407812.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-18T22-40-09.407812.parquet'
- config_name: results
data_files:
- split: 2023_08_29T18_13_52.290314
path:
- results_2023-08-29T18:13:52.290314.parquet
- split: 2023_10_18T22_40_09.407812
path:
- results_2023-10-18T22-40-09.407812.parquet
- split: latest
path:
- results_2023-10-18T22-40-09.407812.parquet
---
# Dataset Card for Evaluation run of NousResearch/CodeLlama-13b-hf
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/NousResearch/CodeLlama-13b-hf
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [NousResearch/CodeLlama-13b-hf](https://huggingface.co/NousResearch/CodeLlama-13b-hf) 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_NousResearch__CodeLlama-13b-hf",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-18T22:40:09.407812](https://huggingface.co/datasets/open-llm-leaderboard/details_NousResearch__CodeLlama-13b-hf/blob/main/results_2023-10-18T22-40-09.407812.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": 0.00033145814652192065,
"f1": 0.05248531879194655,
"f1_stderr": 0.0012515405190332619,
"acc": 0.3964846847094825,
"acc_stderr": 0.011095593973496732
},
"harness|drop|3": {
"em": 0.0010486577181208054,
"em_stderr": 0.00033145814652192065,
"f1": 0.05248531879194655,
"f1_stderr": 0.0012515405190332619
},
"harness|gsm8k|5": {
"acc": 0.12130401819560273,
"acc_stderr": 0.008992888497275572
},
"harness|winogrande|5": {
"acc": 0.6716653512233622,
"acc_stderr": 0.01319829944971789
}
}
```
### 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] |
crumb/Wizard-EvolInstruct70k-k16 | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
- name: cluster
dtype: int64
splits:
- name: train
num_bytes: 131460545
num_examples: 70000
download_size: 69227604
dataset_size: 131460545
---
# Dataset Card for "Wizard-EvolInstruct70k-k16"
`centers.pt` in the files is a 16x384 matrix including the centers of each cluster. I use `sentence-transformers/all-MiniLM-L6-v2` to encode text.
```python
import torch
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
embeddings = torch.tensor(model.encode(sentences))
centers = torch.load("centers.pt")
# mse based cluster choice
clusters = (embeddings - centers).pow(2).mean(1).argmin().tolist()
# or you could load the sklearn kmeans classifier
# todo: documentation for that
# todo: figure out how to do that
# todo: cant you push sklearn classifiers to the hub with some weird code introduced earlier this year or something
``` |
ImagenHub/Text_Guided_Image_Editing | ---
language:
- en
license: cc-by-4.0
size_categories:
- n<1K
task_categories:
- image-to-image
dataset_info:
features:
- name: img_id
dtype: string
- name: turn_index
dtype: int32
- name: source_img
dtype: image
- name: mask_img
dtype: image
- name: instruction
dtype: string
- name: source_global_caption
dtype: string
- name: target_global_caption
dtype: string
- name: target_local_caption
dtype: string
- name: target_img
dtype: image
splits:
- name: dev
num_bytes: 1521276668.0
num_examples: 528
- name: filtered
num_bytes: 504007147.0
num_examples: 179
- name: extra
num_bytes: 709468665.0
num_examples: 249
download_size: 2734685875
dataset_size: 2734752480.0
configs:
- config_name: default
data_files:
- split: dev
path: data/dev-*
- split: filtered
path: data/filtered-*
- split: extra
path: data/extra-*
---
# Dataset Card
Dataset in [ImagenHub](arxiv.org/abs/2310.01596).
# Citation
Please kindly cite our paper if you use our code, data, models or results:
```
@article{ku2023imagenhub,
title={ImagenHub: Standardizing the evaluation of conditional image generation models},
author={Max Ku and Tianle Li and Kai Zhang and Yujie Lu and Xingyu Fu and Wenwen Zhuang and Wenhu Chen},
journal={arXiv preprint arXiv:2310.01596},
year={2023}
}
``` |
cvzion/dqg-3MaybeFinal | ---
dataset_info:
features:
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
splits:
- name: train
num_bytes: 57146
num_examples: 119
download_size: 17819
dataset_size: 57146
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
open-llm-leaderboard/details_freeCS-dot-org__OpenAGI-testing-truthyDPO-1 | ---
pretty_name: Evaluation run of freeCS-dot-org/OpenAGI-testing-truthyDPO-1
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [freeCS-dot-org/OpenAGI-testing-truthyDPO-1](https://huggingface.co/freeCS-dot-org/OpenAGI-testing-truthyDPO-1)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_freeCS-dot-org__OpenAGI-testing-truthyDPO-1\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-02-17T10:15:19.081933](https://huggingface.co/datasets/open-llm-leaderboard/details_freeCS-dot-org__OpenAGI-testing-truthyDPO-1/blob/main/results_2024-02-17T10-15-19.081933.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\": 0.6304857303424044,\n\
\ \"acc_stderr\": 0.03249871750345685,\n \"acc_norm\": 0.635809758335016,\n\
\ \"acc_norm_stderr\": 0.033161164304669005,\n \"mc1\": 0.5361077111383109,\n\
\ \"mc1_stderr\": 0.017457800422268625,\n \"mc2\": 0.7112471524136447,\n\
\ \"mc2_stderr\": 0.014852535681165156\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6322525597269625,\n \"acc_stderr\": 0.014090995618168478,\n\
\ \"acc_norm\": 0.6732081911262798,\n \"acc_norm_stderr\": 0.01370666597558733\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6648078072097192,\n\
\ \"acc_stderr\": 0.004710928569985755,\n \"acc_norm\": 0.8598884684325832,\n\
\ \"acc_norm_stderr\": 0.003463933286063884\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \
\ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6,\n \
\ \"acc_stderr\": 0.04232073695151589,\n \"acc_norm\": 0.6,\n \"\
acc_norm_stderr\": 0.04232073695151589\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\": 0.6,\n\
\ \"acc_stderr\": 0.04923659639173309,\n \"acc_norm\": 0.6,\n \
\ \"acc_norm_stderr\": 0.04923659639173309\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.720754716981132,\n \"acc_stderr\": 0.027611163402399715,\n\
\ \"acc_norm\": 0.720754716981132,\n \"acc_norm_stderr\": 0.027611163402399715\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\": 0.49,\n \"acc_stderr\": 0.05024183937956912,\n \
\ \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956912\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.51,\n \"acc_stderr\": 0.05024183937956911,\n \"acc_norm\": 0.51,\n\
\ \"acc_norm_stderr\": 0.05024183937956911\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \
\ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6011560693641619,\n\
\ \"acc_stderr\": 0.037336266553835096,\n \"acc_norm\": 0.6011560693641619,\n\
\ \"acc_norm_stderr\": 0.037336266553835096\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.4215686274509804,\n \"acc_stderr\": 0.04913595201274498,\n\
\ \"acc_norm\": 0.4215686274509804,\n \"acc_norm_stderr\": 0.04913595201274498\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.79,\n \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\": 0.79,\n\
\ \"acc_norm_stderr\": 0.040936018074033256\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5404255319148936,\n \"acc_stderr\": 0.03257901482099835,\n\
\ \"acc_norm\": 0.5404255319148936,\n \"acc_norm_stderr\": 0.03257901482099835\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.43859649122807015,\n\
\ \"acc_stderr\": 0.04668000738510455,\n \"acc_norm\": 0.43859649122807015,\n\
\ \"acc_norm_stderr\": 0.04668000738510455\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5517241379310345,\n \"acc_stderr\": 0.04144311810878152,\n\
\ \"acc_norm\": 0.5517241379310345,\n \"acc_norm_stderr\": 0.04144311810878152\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.3783068783068783,\n \"acc_stderr\": 0.024976954053155254,\n \"\
acc_norm\": 0.3783068783068783,\n \"acc_norm_stderr\": 0.024976954053155254\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4603174603174603,\n\
\ \"acc_stderr\": 0.04458029125470973,\n \"acc_norm\": 0.4603174603174603,\n\
\ \"acc_norm_stderr\": 0.04458029125470973\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \
\ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.7516129032258064,\n \"acc_stderr\": 0.024580028921481003,\n \"\
acc_norm\": 0.7516129032258064,\n \"acc_norm_stderr\": 0.024580028921481003\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.5270935960591133,\n \"acc_stderr\": 0.03512819077876106,\n \"\
acc_norm\": 0.5270935960591133,\n \"acc_norm_stderr\": 0.03512819077876106\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.67,\n \"acc_stderr\": 0.04725815626252607,\n \"acc_norm\"\
: 0.67,\n \"acc_norm_stderr\": 0.04725815626252607\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7575757575757576,\n \"acc_stderr\": 0.03346409881055953,\n\
\ \"acc_norm\": 0.7575757575757576,\n \"acc_norm_stderr\": 0.03346409881055953\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7878787878787878,\n \"acc_stderr\": 0.029126522834586815,\n \"\
acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.029126522834586815\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8601036269430051,\n \"acc_stderr\": 0.02503387058301518,\n\
\ \"acc_norm\": 0.8601036269430051,\n \"acc_norm_stderr\": 0.02503387058301518\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6358974358974359,\n \"acc_stderr\": 0.024396672985094767,\n\
\ \"acc_norm\": 0.6358974358974359,\n \"acc_norm_stderr\": 0.024396672985094767\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.3111111111111111,\n \"acc_stderr\": 0.028226446749683512,\n \
\ \"acc_norm\": 0.3111111111111111,\n \"acc_norm_stderr\": 0.028226446749683512\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6932773109243697,\n \"acc_stderr\": 0.029953823891887044,\n\
\ \"acc_norm\": 0.6932773109243697,\n \"acc_norm_stderr\": 0.029953823891887044\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.3708609271523179,\n \"acc_stderr\": 0.03943966699183629,\n \"\
acc_norm\": 0.3708609271523179,\n \"acc_norm_stderr\": 0.03943966699183629\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8330275229357799,\n \"acc_stderr\": 0.015990154885073406,\n \"\
acc_norm\": 0.8330275229357799,\n \"acc_norm_stderr\": 0.015990154885073406\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5231481481481481,\n \"acc_stderr\": 0.03406315360711507,\n \"\
acc_norm\": 0.5231481481481481,\n \"acc_norm_stderr\": 0.03406315360711507\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.803921568627451,\n \"acc_stderr\": 0.027865942286639325,\n \"\
acc_norm\": 0.803921568627451,\n \"acc_norm_stderr\": 0.027865942286639325\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8143459915611815,\n \"acc_stderr\": 0.025310495376944856,\n \
\ \"acc_norm\": 0.8143459915611815,\n \"acc_norm_stderr\": 0.025310495376944856\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6771300448430493,\n\
\ \"acc_stderr\": 0.031381476375754995,\n \"acc_norm\": 0.6771300448430493,\n\
\ \"acc_norm_stderr\": 0.031381476375754995\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7480916030534351,\n \"acc_stderr\": 0.03807387116306085,\n\
\ \"acc_norm\": 0.7480916030534351,\n \"acc_norm_stderr\": 0.03807387116306085\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.8181818181818182,\n \"acc_stderr\": 0.03520893951097653,\n \"\
acc_norm\": 0.8181818181818182,\n \"acc_norm_stderr\": 0.03520893951097653\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.75,\n\
\ \"acc_stderr\": 0.04186091791394607,\n \"acc_norm\": 0.75,\n \
\ \"acc_norm_stderr\": 0.04186091791394607\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7300613496932515,\n \"acc_stderr\": 0.034878251684978906,\n\
\ \"acc_norm\": 0.7300613496932515,\n \"acc_norm_stderr\": 0.034878251684978906\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4732142857142857,\n\
\ \"acc_stderr\": 0.047389751192741546,\n \"acc_norm\": 0.4732142857142857,\n\
\ \"acc_norm_stderr\": 0.047389751192741546\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7669902912621359,\n \"acc_stderr\": 0.04185832598928315,\n\
\ \"acc_norm\": 0.7669902912621359,\n \"acc_norm_stderr\": 0.04185832598928315\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8931623931623932,\n\
\ \"acc_stderr\": 0.02023714900899093,\n \"acc_norm\": 0.8931623931623932,\n\
\ \"acc_norm_stderr\": 0.02023714900899093\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.68,\n \"acc_stderr\": 0.04688261722621504,\n \
\ \"acc_norm\": 0.68,\n \"acc_norm_stderr\": 0.04688261722621504\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8237547892720306,\n\
\ \"acc_stderr\": 0.013625556907993464,\n \"acc_norm\": 0.8237547892720306,\n\
\ \"acc_norm_stderr\": 0.013625556907993464\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.6936416184971098,\n \"acc_stderr\": 0.024818350129436593,\n\
\ \"acc_norm\": 0.6936416184971098,\n \"acc_norm_stderr\": 0.024818350129436593\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.376536312849162,\n\
\ \"acc_stderr\": 0.016204672385106603,\n \"acc_norm\": 0.376536312849162,\n\
\ \"acc_norm_stderr\": 0.016204672385106603\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7222222222222222,\n \"acc_stderr\": 0.025646863097137897,\n\
\ \"acc_norm\": 0.7222222222222222,\n \"acc_norm_stderr\": 0.025646863097137897\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6945337620578779,\n\
\ \"acc_stderr\": 0.02616058445014045,\n \"acc_norm\": 0.6945337620578779,\n\
\ \"acc_norm_stderr\": 0.02616058445014045\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7283950617283951,\n \"acc_stderr\": 0.02474862449053737,\n\
\ \"acc_norm\": 0.7283950617283951,\n \"acc_norm_stderr\": 0.02474862449053737\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.475177304964539,\n \"acc_stderr\": 0.029790719243829727,\n \
\ \"acc_norm\": 0.475177304964539,\n \"acc_norm_stderr\": 0.029790719243829727\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.45045632333767927,\n\
\ \"acc_stderr\": 0.012707390438502346,\n \"acc_norm\": 0.45045632333767927,\n\
\ \"acc_norm_stderr\": 0.012707390438502346\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6617647058823529,\n \"acc_stderr\": 0.028739328513983576,\n\
\ \"acc_norm\": 0.6617647058823529,\n \"acc_norm_stderr\": 0.028739328513983576\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6552287581699346,\n \"acc_stderr\": 0.019228322018696647,\n \
\ \"acc_norm\": 0.6552287581699346,\n \"acc_norm_stderr\": 0.019228322018696647\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7,\n\
\ \"acc_stderr\": 0.04389311454644287,\n \"acc_norm\": 0.7,\n \
\ \"acc_norm_stderr\": 0.04389311454644287\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7061224489795919,\n \"acc_stderr\": 0.029162738410249772,\n\
\ \"acc_norm\": 0.7061224489795919,\n \"acc_norm_stderr\": 0.029162738410249772\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.7810945273631841,\n\
\ \"acc_stderr\": 0.029239174636647,\n \"acc_norm\": 0.7810945273631841,\n\
\ \"acc_norm_stderr\": 0.029239174636647\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.84,\n \"acc_stderr\": 0.03684529491774708,\n \
\ \"acc_norm\": 0.84,\n \"acc_norm_stderr\": 0.03684529491774708\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5120481927710844,\n\
\ \"acc_stderr\": 0.03891364495835817,\n \"acc_norm\": 0.5120481927710844,\n\
\ \"acc_norm_stderr\": 0.03891364495835817\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8304093567251462,\n \"acc_stderr\": 0.02878210810540171,\n\
\ \"acc_norm\": 0.8304093567251462,\n \"acc_norm_stderr\": 0.02878210810540171\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.5361077111383109,\n\
\ \"mc1_stderr\": 0.017457800422268625,\n \"mc2\": 0.7112471524136447,\n\
\ \"mc2_stderr\": 0.014852535681165156\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8121546961325967,\n \"acc_stderr\": 0.010977481103435091\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.3707354056103108,\n \
\ \"acc_stderr\": 0.01330426770545843\n }\n}\n```"
repo_url: https://huggingface.co/freeCS-dot-org/OpenAGI-testing-truthyDPO-1
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_02_17T10_15_19.081933
path:
- '**/details_harness|arc:challenge|25_2024-02-17T10-15-19.081933.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-02-17T10-15-19.081933.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_02_17T10_15_19.081933
path:
- '**/details_harness|gsm8k|5_2024-02-17T10-15-19.081933.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-02-17T10-15-19.081933.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_02_17T10_15_19.081933
path:
- '**/details_harness|hellaswag|10_2024-02-17T10-15-19.081933.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-02-17T10-15-19.081933.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_02_17T10_15_19.081933
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-17T10-15-19.081933.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-17T10-15-19.081933.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-17T10-15-19.081933.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_02_17T10_15_19.081933
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-17T10-15-19.081933.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-17T10-15-19.081933.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_02_17T10_15_19.081933
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-17T10-15-19.081933.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-17T10-15-19.081933.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_02_17T10_15_19.081933
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-17T10-15-19.081933.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-17T10-15-19.081933.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_02_17T10_15_19.081933
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-17T10-15-19.081933.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-17T10-15-19.081933.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_02_17T10_15_19.081933
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-17T10-15-19.081933.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-17T10-15-19.081933.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_02_17T10_15_19.081933
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-17T10-15-19.081933.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-17T10-15-19.081933.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_02_17T10_15_19.081933
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-17T10-15-19.081933.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-17T10-15-19.081933.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_02_17T10_15_19.081933
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-17T10-15-19.081933.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-17T10-15-19.081933.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_02_17T10_15_19.081933
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-17T10-15-19.081933.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-17T10-15-19.081933.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_02_17T10_15_19.081933
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-17T10-15-19.081933.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-17T10-15-19.081933.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_02_17T10_15_19.081933
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-17T10-15-19.081933.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-17T10-15-19.081933.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_02_17T10_15_19.081933
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-17T10-15-19.081933.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-17T10-15-19.081933.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_02_17T10_15_19.081933
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-17T10-15-19.081933.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-17T10-15-19.081933.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_02_17T10_15_19.081933
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-17T10-15-19.081933.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-17T10-15-19.081933.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_02_17T10_15_19.081933
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-17T10-15-19.081933.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-17T10-15-19.081933.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_02_17T10_15_19.081933
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-17T10-15-19.081933.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-17T10-15-19.081933.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_02_17T10_15_19.081933
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-17T10-15-19.081933.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-17T10-15-19.081933.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_02_17T10_15_19.081933
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-17T10-15-19.081933.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-17T10-15-19.081933.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_02_17T10_15_19.081933
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-17T10-15-19.081933.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-17T10-15-19.081933.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_02_17T10_15_19.081933
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-17T10-15-19.081933.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-17T10-15-19.081933.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_02_17T10_15_19.081933
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-17T10-15-19.081933.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-17T10-15-19.081933.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_02_17T10_15_19.081933
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-17T10-15-19.081933.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-17T10-15-19.081933.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_02_17T10_15_19.081933
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-17T10-15-19.081933.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-17T10-15-19.081933.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_02_17T10_15_19.081933
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-17T10-15-19.081933.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-17T10-15-19.081933.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_02_17T10_15_19.081933
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-17T10-15-19.081933.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-17T10-15-19.081933.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_02_17T10_15_19.081933
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-17T10-15-19.081933.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-17T10-15-19.081933.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_02_17T10_15_19.081933
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-17T10-15-19.081933.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-17T10-15-19.081933.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_02_17T10_15_19.081933
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-17T10-15-19.081933.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-17T10-15-19.081933.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_02_17T10_15_19.081933
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-17T10-15-19.081933.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-17T10-15-19.081933.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_02_17T10_15_19.081933
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-17T10-15-19.081933.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-17T10-15-19.081933.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_02_17T10_15_19.081933
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-17T10-15-19.081933.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-17T10-15-19.081933.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_02_17T10_15_19.081933
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-17T10-15-19.081933.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-17T10-15-19.081933.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_02_17T10_15_19.081933
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-17T10-15-19.081933.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-17T10-15-19.081933.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_02_17T10_15_19.081933
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-17T10-15-19.081933.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-17T10-15-19.081933.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_02_17T10_15_19.081933
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-17T10-15-19.081933.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-17T10-15-19.081933.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_02_17T10_15_19.081933
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-17T10-15-19.081933.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-17T10-15-19.081933.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_02_17T10_15_19.081933
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-17T10-15-19.081933.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-17T10-15-19.081933.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_02_17T10_15_19.081933
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-17T10-15-19.081933.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-17T10-15-19.081933.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_02_17T10_15_19.081933
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-17T10-15-19.081933.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-17T10-15-19.081933.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_02_17T10_15_19.081933
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-17T10-15-19.081933.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-17T10-15-19.081933.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_02_17T10_15_19.081933
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-17T10-15-19.081933.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-17T10-15-19.081933.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_02_17T10_15_19.081933
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-17T10-15-19.081933.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-17T10-15-19.081933.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_02_17T10_15_19.081933
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-17T10-15-19.081933.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-17T10-15-19.081933.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_02_17T10_15_19.081933
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-17T10-15-19.081933.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-17T10-15-19.081933.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_02_17T10_15_19.081933
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-17T10-15-19.081933.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-17T10-15-19.081933.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_02_17T10_15_19.081933
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-17T10-15-19.081933.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-17T10-15-19.081933.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_02_17T10_15_19.081933
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-17T10-15-19.081933.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-17T10-15-19.081933.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_02_17T10_15_19.081933
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-17T10-15-19.081933.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-17T10-15-19.081933.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_02_17T10_15_19.081933
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-17T10-15-19.081933.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-17T10-15-19.081933.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_02_17T10_15_19.081933
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-17T10-15-19.081933.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-17T10-15-19.081933.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_02_17T10_15_19.081933
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-17T10-15-19.081933.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-17T10-15-19.081933.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_02_17T10_15_19.081933
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-17T10-15-19.081933.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-17T10-15-19.081933.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_02_17T10_15_19.081933
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-17T10-15-19.081933.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-17T10-15-19.081933.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_02_17T10_15_19.081933
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-17T10-15-19.081933.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-17T10-15-19.081933.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_02_17T10_15_19.081933
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-17T10-15-19.081933.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-17T10-15-19.081933.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_02_17T10_15_19.081933
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-17T10-15-19.081933.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-17T10-15-19.081933.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_02_17T10_15_19.081933
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-17T10-15-19.081933.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-17T10-15-19.081933.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_02_17T10_15_19.081933
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-17T10-15-19.081933.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-17T10-15-19.081933.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_02_17T10_15_19.081933
path:
- '**/details_harness|winogrande|5_2024-02-17T10-15-19.081933.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-02-17T10-15-19.081933.parquet'
- config_name: results
data_files:
- split: 2024_02_17T10_15_19.081933
path:
- results_2024-02-17T10-15-19.081933.parquet
- split: latest
path:
- results_2024-02-17T10-15-19.081933.parquet
---
# Dataset Card for Evaluation run of freeCS-dot-org/OpenAGI-testing-truthyDPO-1
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [freeCS-dot-org/OpenAGI-testing-truthyDPO-1](https://huggingface.co/freeCS-dot-org/OpenAGI-testing-truthyDPO-1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_freeCS-dot-org__OpenAGI-testing-truthyDPO-1",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-02-17T10:15:19.081933](https://huggingface.co/datasets/open-llm-leaderboard/details_freeCS-dot-org__OpenAGI-testing-truthyDPO-1/blob/main/results_2024-02-17T10-15-19.081933.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": 0.6304857303424044,
"acc_stderr": 0.03249871750345685,
"acc_norm": 0.635809758335016,
"acc_norm_stderr": 0.033161164304669005,
"mc1": 0.5361077111383109,
"mc1_stderr": 0.017457800422268625,
"mc2": 0.7112471524136447,
"mc2_stderr": 0.014852535681165156
},
"harness|arc:challenge|25": {
"acc": 0.6322525597269625,
"acc_stderr": 0.014090995618168478,
"acc_norm": 0.6732081911262798,
"acc_norm_stderr": 0.01370666597558733
},
"harness|hellaswag|10": {
"acc": 0.6648078072097192,
"acc_stderr": 0.004710928569985755,
"acc_norm": 0.8598884684325832,
"acc_norm_stderr": 0.003463933286063884
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.32,
"acc_stderr": 0.046882617226215034,
"acc_norm": 0.32,
"acc_norm_stderr": 0.046882617226215034
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6,
"acc_stderr": 0.04232073695151589,
"acc_norm": 0.6,
"acc_norm_stderr": 0.04232073695151589
},
"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": 0.6,
"acc_stderr": 0.04923659639173309,
"acc_norm": 0.6,
"acc_norm_stderr": 0.04923659639173309
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.720754716981132,
"acc_stderr": 0.027611163402399715,
"acc_norm": 0.720754716981132,
"acc_norm_stderr": 0.027611163402399715
},
"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": 0.49,
"acc_stderr": 0.05024183937956912,
"acc_norm": 0.49,
"acc_norm_stderr": 0.05024183937956912
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.51,
"acc_stderr": 0.05024183937956911,
"acc_norm": 0.51,
"acc_norm_stderr": 0.05024183937956911
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.31,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.31,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6011560693641619,
"acc_stderr": 0.037336266553835096,
"acc_norm": 0.6011560693641619,
"acc_norm_stderr": 0.037336266553835096
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.4215686274509804,
"acc_stderr": 0.04913595201274498,
"acc_norm": 0.4215686274509804,
"acc_norm_stderr": 0.04913595201274498
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.79,
"acc_stderr": 0.040936018074033256,
"acc_norm": 0.79,
"acc_norm_stderr": 0.040936018074033256
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5404255319148936,
"acc_stderr": 0.03257901482099835,
"acc_norm": 0.5404255319148936,
"acc_norm_stderr": 0.03257901482099835
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.43859649122807015,
"acc_stderr": 0.04668000738510455,
"acc_norm": 0.43859649122807015,
"acc_norm_stderr": 0.04668000738510455
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5517241379310345,
"acc_stderr": 0.04144311810878152,
"acc_norm": 0.5517241379310345,
"acc_norm_stderr": 0.04144311810878152
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.3783068783068783,
"acc_stderr": 0.024976954053155254,
"acc_norm": 0.3783068783068783,
"acc_norm_stderr": 0.024976954053155254
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.4603174603174603,
"acc_stderr": 0.04458029125470973,
"acc_norm": 0.4603174603174603,
"acc_norm_stderr": 0.04458029125470973
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.32,
"acc_stderr": 0.046882617226215034,
"acc_norm": 0.32,
"acc_norm_stderr": 0.046882617226215034
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7516129032258064,
"acc_stderr": 0.024580028921481003,
"acc_norm": 0.7516129032258064,
"acc_norm_stderr": 0.024580028921481003
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5270935960591133,
"acc_stderr": 0.03512819077876106,
"acc_norm": 0.5270935960591133,
"acc_norm_stderr": 0.03512819077876106
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.67,
"acc_stderr": 0.04725815626252607,
"acc_norm": 0.67,
"acc_norm_stderr": 0.04725815626252607
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7575757575757576,
"acc_stderr": 0.03346409881055953,
"acc_norm": 0.7575757575757576,
"acc_norm_stderr": 0.03346409881055953
},
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}
```
## Dataset Details
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