datasetId stringlengths 2 117 | card stringlengths 19 1.01M |
|---|---|
CyberHarem/dewey_azurlane | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of dewey/デューイ/杜威 (Azur Lane)
This is the dataset of dewey/デューイ/杜威 (Azur Lane), containing 23 images and their tags.
The core tags of this character are `long_hair, purple_hair, yellow_eyes, hair_between_eyes, breasts, bangs, hat, small_breasts, very_long_hair`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 23 | 24.80 MiB | [Download](https://huggingface.co/datasets/CyberHarem/dewey_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 23 | 14.66 MiB | [Download](https://huggingface.co/datasets/CyberHarem/dewey_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 51 | 30.56 MiB | [Download](https://huggingface.co/datasets/CyberHarem/dewey_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 23 | 21.52 MiB | [Download](https://huggingface.co/datasets/CyberHarem/dewey_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 51 | 42.86 MiB | [Download](https://huggingface.co/datasets/CyberHarem/dewey_azurlane/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/dewey_azurlane',
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, blush, solo, looking_at_viewer, bare_shoulders, elbow_gloves, white_gloves, white_thighhighs, white_headwear, pleated_skirt, simple_background, sleeveless, blue_skirt, white_background |
| 1 | 6 |  |  |  |  |  | 1girl, solo, looking_at_viewer, twintails, hair_bow, hair_ornament, simple_background, ass, bare_shoulders, black_bikini, brown_eyes, frilled_bikini, nipples, open_mouth, sidelocks, white_background |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blush | solo | looking_at_viewer | bare_shoulders | elbow_gloves | white_gloves | white_thighhighs | white_headwear | pleated_skirt | simple_background | sleeveless | blue_skirt | white_background | twintails | hair_bow | hair_ornament | ass | black_bikini | brown_eyes | frilled_bikini | nipples | open_mouth | sidelocks |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:-------|:--------------------|:-----------------|:---------------|:---------------|:-------------------|:-----------------|:----------------|:--------------------|:-------------|:-------------|:-------------------|:------------|:-----------|:----------------|:------|:---------------|:-------------|:-----------------|:----------|:-------------|:------------|
| 0 | 15 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | |
| 1 | 6 |  |  |  |  |  | X | | X | X | X | | | | | | X | | | X | X | X | X | X | X | X | X | X | X | X |
|
ilustraviz/training_sofa | ---
license: mit
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 378645.0
num_examples: 5
download_size: 341812
dataset_size: 378645.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
detek/robot-task-planning | ---
task_categories:
- text2text-generation
language:
- en
tags:
- task planning
- robot
- cobot
- subtasks
--- |
jat-project/jat-dataset | ---
annotations_creators:
- found
- machine-generated
license: apache-2.0
source_datasets:
- conceptual-captions
- ok-vqa
- oscar
task_categories:
- reinforcement-learning
- text-generation
- question-answering
pretty_name: JAT-dataset
configs:
- config_name: atari-alien
data_files:
- split: train
path: atari-alien/train-*
- split: test
path: atari-alien/test-*
- config_name: atari-amidar
data_files:
- split: train
path: atari-amidar/train-*
- split: test
path: atari-amidar/test-*
- config_name: atari-assault
data_files:
- split: train
path: atari-assault/train-*
- split: test
path: atari-assault/test-*
- config_name: atari-asterix
data_files:
- split: train
path: atari-asterix/train-*
- split: test
path: atari-asterix/test-*
- config_name: atari-asteroids
data_files:
- split: train
path: atari-asteroids/train-*
- split: test
path: atari-asteroids/test-*
- config_name: atari-atlantis
data_files:
- split: train
path: atari-atlantis/train-*
- split: test
path: atari-atlantis/test-*
- config_name: atari-bankheist
data_files:
- split: train
path: atari-bankheist/train-*
- split: test
path: atari-bankheist/test-*
- config_name: atari-battlezone
data_files:
- split: train
path: atari-battlezone/train-*
- split: test
path: atari-battlezone/test-*
- config_name: atari-beamrider
data_files:
- split: train
path: atari-beamrider/train-*
- split: test
path: atari-beamrider/test-*
- config_name: atari-berzerk
data_files:
- split: train
path: atari-berzerk/train-*
- split: test
path: atari-berzerk/test-*
- config_name: atari-bowling
data_files:
- split: train
path: atari-bowling/train-*
- split: test
path: atari-bowling/test-*
- config_name: atari-boxing
data_files:
- split: train
path: atari-boxing/train-*
- split: test
path: atari-boxing/test-*
- config_name: atari-breakout
data_files:
- split: train
path: atari-breakout/train-*
- split: test
path: atari-breakout/test-*
- config_name: atari-centipede
data_files:
- split: train
path: atari-centipede/train-*
- split: test
path: atari-centipede/test-*
- config_name: atari-choppercommand
data_files:
- split: train
path: atari-choppercommand/train-*
- split: test
path: atari-choppercommand/test-*
- config_name: atari-crazyclimber
data_files:
- split: train
path: atari-crazyclimber/train-*
- split: test
path: atari-crazyclimber/test-*
- config_name: atari-defender
data_files:
- split: train
path: atari-defender/train-*
- split: test
path: atari-defender/test-*
- config_name: atari-demonattack
data_files:
- split: train
path: atari-demonattack/train-*
- split: test
path: atari-demonattack/test-*
- config_name: atari-doubledunk
data_files:
- split: test
path: atari-doubledunk/test-*
- split: train
path: atari-doubledunk/train-*
- config_name: atari-enduro
data_files:
- split: train
path: atari-enduro/train-*
- split: test
path: atari-enduro/test-*
- config_name: atari-fishingderby
data_files:
- split: train
path: atari-fishingderby/train-*
- split: test
path: atari-fishingderby/test-*
- config_name: atari-freeway
data_files:
- split: train
path: atari-freeway/train-*
- split: test
path: atari-freeway/test-*
- config_name: atari-frostbite
data_files:
- split: train
path: atari-frostbite/train-*
- split: test
path: atari-frostbite/test-*
- config_name: atari-gopher
data_files:
- split: train
path: atari-gopher/train-*
- split: test
path: atari-gopher/test-*
- config_name: atari-gravitar
data_files:
- split: train
path: atari-gravitar/train-*
- split: test
path: atari-gravitar/test-*
- config_name: atari-hero
data_files:
- split: train
path: atari-hero/train-*
- split: test
path: atari-hero/test-*
- config_name: atari-icehockey
data_files:
- split: train
path: atari-icehockey/train-*
- split: test
path: atari-icehockey/test-*
- config_name: atari-jamesbond
data_files:
- split: train
path: atari-jamesbond/train-*
- split: test
path: atari-jamesbond/test-*
- config_name: atari-kangaroo
data_files:
- split: train
path: atari-kangaroo/train-*
- split: test
path: atari-kangaroo/test-*
- config_name: atari-krull
data_files:
- split: train
path: atari-krull/train-*
- split: test
path: atari-krull/test-*
- config_name: atari-kungfumaster
data_files:
- split: train
path: atari-kungfumaster/train-*
- split: test
path: atari-kungfumaster/test-*
- config_name: atari-montezumarevenge
data_files:
- split: train
path: atari-montezumarevenge/train-*
- split: test
path: atari-montezumarevenge/test-*
- config_name: atari-mspacman
data_files:
- split: train
path: atari-mspacman/train-*
- split: test
path: atari-mspacman/test-*
- config_name: atari-namethisgame
data_files:
- split: train
path: atari-namethisgame/train-*
- split: test
path: atari-namethisgame/test-*
- config_name: atari-phoenix
data_files:
- split: train
path: atari-phoenix/train-*
- split: test
path: atari-phoenix/test-*
- config_name: atari-pitfall
data_files:
- split: train
path: atari-pitfall/train-*
- split: test
path: atari-pitfall/test-*
- config_name: atari-pong
data_files:
- split: test
path: atari-pong/test-*
- split: train
path: atari-pong/train-*
- config_name: atari-privateeye
data_files:
- split: test
path: atari-privateeye/test-*
- split: train
path: atari-privateeye/train-*
- config_name: atari-qbert
data_files:
- split: test
path: atari-qbert/test-*
- split: train
path: atari-qbert/train-*
- config_name: atari-riverraid
data_files:
- split: test
path: atari-riverraid/test-*
- split: train
path: atari-riverraid/train-*
- config_name: atari-roadrunner
data_files:
- split: test
path: atari-roadrunner/test-*
- split: train
path: atari-roadrunner/train-*
- config_name: atari-robotank
data_files:
- split: test
path: atari-robotank/test-*
- split: train
path: atari-robotank/train-*
- config_name: atari-seaquest
data_files:
- split: test
path: atari-seaquest/test-*
- split: train
path: atari-seaquest/train-*
- config_name: atari-skiing
data_files:
- split: train
path: atari-skiing/train-*
- split: test
path: atari-skiing/test-*
- config_name: atari-solaris
data_files:
- split: train
path: atari-solaris/train-*
- split: test
path: atari-solaris/test-*
- config_name: atari-spaceinvaders
data_files:
- split: train
path: atari-spaceinvaders/train-*
- split: test
path: atari-spaceinvaders/test-*
- config_name: atari-stargunner
data_files:
- split: train
path: atari-stargunner/train-*
- split: test
path: atari-stargunner/test-*
- config_name: atari-surround
data_files:
- split: train
path: atari-surround/train-*
- split: test
path: atari-surround/test-*
- config_name: atari-tennis
data_files:
- split: train
path: atari-tennis/train-*
- split: test
path: atari-tennis/test-*
- config_name: atari-timepilot
data_files:
- split: train
path: atari-timepilot/train-*
- split: test
path: atari-timepilot/test-*
- config_name: atari-tutankham
data_files:
- split: train
path: atari-tutankham/train-*
- split: test
path: atari-tutankham/test-*
- config_name: atari-upndown
data_files:
- split: train
path: atari-upndown/train-*
- split: test
path: atari-upndown/test-*
- config_name: atari-venture
data_files:
- split: test
path: atari-venture/test-*
- split: train
path: atari-venture/train-*
- config_name: atari-videopinball
data_files:
- split: test
path: atari-videopinball/test-*
- split: train
path: atari-videopinball/train-*
- config_name: atari-wizardofwor
data_files:
- split: test
path: atari-wizardofwor/test-*
- split: train
path: atari-wizardofwor/train-*
- config_name: atari-yarsrevenge
data_files:
- split: test
path: atari-yarsrevenge/test-*
- split: train
path: atari-yarsrevenge/train-*
- config_name: atari-zaxxon
data_files:
- split: test
path: atari-zaxxon/test-*
- split: train
path: atari-zaxxon/train-*
- config_name: babyai-action-obj-door
data_files:
- split: train
path: babyai-action-obj-door/train-*
- split: test
path: babyai-action-obj-door/test-*
- config_name: babyai-blocked-unlock-pickup
data_files:
- split: test
path: babyai-blocked-unlock-pickup/test-*
- split: train
path: babyai-blocked-unlock-pickup/train-*
- config_name: babyai-boss-level
data_files:
- split: test
path: babyai-boss-level/test-*
- split: train
path: babyai-boss-level/train-*
- config_name: babyai-boss-level-no-unlock
data_files:
- split: test
path: babyai-boss-level-no-unlock/test-*
- split: train
path: babyai-boss-level-no-unlock/train-*
- config_name: babyai-find-obj-s5
data_files:
- split: train
path: babyai-find-obj-s5/train-*
- split: test
path: babyai-find-obj-s5/test-*
- config_name: babyai-go-to
data_files:
- split: train
path: babyai-go-to/train-*
- split: test
path: babyai-go-to/test-*
- config_name: babyai-go-to-door
data_files:
- split: train
path: babyai-go-to-door/train-*
- split: test
path: babyai-go-to-door/test-*
- config_name: babyai-go-to-imp-unlock
data_files:
- split: train
path: babyai-go-to-imp-unlock/train-*
- split: test
path: babyai-go-to-imp-unlock/test-*
- config_name: babyai-go-to-local
data_files:
- split: train
path: babyai-go-to-local/train-*
- split: test
path: babyai-go-to-local/test-*
- config_name: babyai-go-to-obj
data_files:
- split: train
path: babyai-go-to-obj/train-*
- split: test
path: babyai-go-to-obj/test-*
- config_name: babyai-go-to-obj-door
data_files:
- split: train
path: babyai-go-to-obj-door/train-*
- split: test
path: babyai-go-to-obj-door/test-*
- config_name: babyai-go-to-red-ball
data_files:
- split: train
path: babyai-go-to-red-ball/train-*
- split: test
path: babyai-go-to-red-ball/test-*
- config_name: babyai-go-to-red-ball-grey
data_files:
- split: train
path: babyai-go-to-red-ball-grey/train-*
- split: test
path: babyai-go-to-red-ball-grey/test-*
- config_name: babyai-go-to-red-ball-no-dists
data_files:
- split: train
path: babyai-go-to-red-ball-no-dists/train-*
- split: test
path: babyai-go-to-red-ball-no-dists/test-*
- config_name: babyai-go-to-red-blue-ball
data_files:
- split: train
path: babyai-go-to-red-blue-ball/train-*
- split: test
path: babyai-go-to-red-blue-ball/test-*
- config_name: babyai-go-to-seq
data_files:
- split: train
path: babyai-go-to-seq/train-*
- split: test
path: babyai-go-to-seq/test-*
- config_name: babyai-key-corridor
data_files:
- split: test
path: babyai-key-corridor/test-*
- split: train
path: babyai-key-corridor/train-*
- config_name: babyai-mini-boss-level
data_files:
- split: test
path: babyai-mini-boss-level/test-*
- split: train
path: babyai-mini-boss-level/train-*
- config_name: babyai-move-two-across-s8n9
data_files:
- split: test
path: babyai-move-two-across-s8n9/test-*
- split: train
path: babyai-move-two-across-s8n9/train-*
- config_name: babyai-one-room-s8
data_files:
- split: test
path: babyai-one-room-s8/test-*
- split: train
path: babyai-one-room-s8/train-*
- config_name: babyai-open
data_files:
- split: test
path: babyai-open/test-*
- split: train
path: babyai-open/train-*
- config_name: babyai-open-door
data_files:
- split: test
path: babyai-open-door/test-*
- split: train
path: babyai-open-door/train-*
- config_name: babyai-open-doors-order-n4
data_files:
- split: test
path: babyai-open-doors-order-n4/test-*
- split: train
path: babyai-open-doors-order-n4/train-*
- config_name: babyai-open-red-door
data_files:
- split: test
path: babyai-open-red-door/test-*
- split: train
path: babyai-open-red-door/train-*
- config_name: babyai-open-two-doors
data_files:
- split: test
path: babyai-open-two-doors/test-*
- split: train
path: babyai-open-two-doors/train-*
- config_name: babyai-pickup
data_files:
- split: test
path: babyai-pickup/test-*
- split: train
path: babyai-pickup/train-*
- config_name: babyai-pickup-above
data_files:
- split: test
path: babyai-pickup-above/test-*
- split: train
path: babyai-pickup-above/train-*
- config_name: babyai-pickup-dist
data_files:
- split: test
path: babyai-pickup-dist/test-*
- split: train
path: babyai-pickup-dist/train-*
- config_name: babyai-pickup-loc
data_files:
- split: test
path: babyai-pickup-loc/test-*
- split: train
path: babyai-pickup-loc/train-*
- config_name: babyai-put-next
data_files:
- split: train
path: babyai-put-next/train-*
- split: test
path: babyai-put-next/test-*
- config_name: babyai-put-next-local
data_files:
- split: train
path: babyai-put-next-local/train-*
- split: test
path: babyai-put-next-local/test-*
- config_name: babyai-synth
data_files:
- split: test
path: babyai-synth/test-*
- split: train
path: babyai-synth/train-*
- config_name: babyai-synth-loc
data_files:
- split: test
path: babyai-synth-loc/test-*
- split: train
path: babyai-synth-loc/train-*
- config_name: babyai-synth-seq
data_files:
- split: test
path: babyai-synth-seq/test-*
- split: train
path: babyai-synth-seq/train-*
- config_name: babyai-unblock-pickup
data_files:
- split: test
path: babyai-unblock-pickup/test-*
- split: train
path: babyai-unblock-pickup/train-*
- config_name: babyai-unlock
data_files:
- split: train
path: babyai-unlock/train-*
- split: test
path: babyai-unlock/test-*
- config_name: babyai-unlock-local
data_files:
- split: test
path: babyai-unlock-local/test-*
- split: train
path: babyai-unlock-local/train-*
- config_name: babyai-unlock-pickup
data_files:
- split: test
path: babyai-unlock-pickup/test-*
- split: train
path: babyai-unlock-pickup/train-*
- config_name: babyai-unlock-to-unlock
data_files:
- split: train
path: babyai-unlock-to-unlock/train-*
- split: test
path: babyai-unlock-to-unlock/test-*
- config_name: conceptual-captions
data_files:
- split: test
path: conceptual-captions/test-*
- split: train
path: conceptual-captions/train-*
- config_name: metaworld-assembly
data_files:
- split: train
path: metaworld-assembly/train-*
- split: test
path: metaworld-assembly/test-*
- config_name: metaworld-basketball
data_files:
- split: train
path: metaworld-basketball/train-*
- split: test
path: metaworld-basketball/test-*
- config_name: metaworld-bin-picking
data_files:
- split: train
path: metaworld-bin-picking/train-*
- split: test
path: metaworld-bin-picking/test-*
- config_name: metaworld-box-close
data_files:
- split: train
path: metaworld-box-close/train-*
- split: test
path: metaworld-box-close/test-*
- config_name: metaworld-button-press
data_files:
- split: train
path: metaworld-button-press/train-*
- split: test
path: metaworld-button-press/test-*
- config_name: metaworld-button-press-topdown
data_files:
- split: train
path: metaworld-button-press-topdown/train-*
- split: test
path: metaworld-button-press-topdown/test-*
- config_name: metaworld-button-press-topdown-wall
data_files:
- split: train
path: metaworld-button-press-topdown-wall/train-*
- split: test
path: metaworld-button-press-topdown-wall/test-*
- config_name: metaworld-button-press-wall
data_files:
- split: train
path: metaworld-button-press-wall/train-*
- split: test
path: metaworld-button-press-wall/test-*
- config_name: metaworld-coffee-button
data_files:
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path: metaworld-coffee-button/train-*
- split: test
path: metaworld-coffee-button/test-*
- config_name: metaworld-coffee-pull
data_files:
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path: metaworld-coffee-pull/train-*
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path: metaworld-coffee-pull/test-*
- config_name: metaworld-coffee-push
data_files:
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path: metaworld-coffee-push/train-*
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path: metaworld-coffee-push/test-*
- config_name: metaworld-dial-turn
data_files:
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path: metaworld-dial-turn/train-*
- split: test
path: metaworld-dial-turn/test-*
- config_name: metaworld-disassemble
data_files:
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path: metaworld-disassemble/train-*
- split: test
path: metaworld-disassemble/test-*
- config_name: metaworld-door-close
data_files:
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path: metaworld-door-close/train-*
- split: test
path: metaworld-door-close/test-*
- config_name: metaworld-door-lock
data_files:
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path: metaworld-door-lock/train-*
- split: test
path: metaworld-door-lock/test-*
- config_name: metaworld-door-open
data_files:
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path: metaworld-door-open/train-*
- split: test
path: metaworld-door-open/test-*
- config_name: metaworld-door-unlock
data_files:
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path: metaworld-door-unlock/train-*
- split: test
path: metaworld-door-unlock/test-*
- config_name: metaworld-drawer-close
data_files:
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path: metaworld-drawer-close/train-*
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path: metaworld-drawer-close/test-*
- config_name: metaworld-drawer-open
data_files:
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path: metaworld-drawer-open/train-*
- split: test
path: metaworld-drawer-open/test-*
- config_name: metaworld-faucet-close
data_files:
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path: metaworld-faucet-close/train-*
- split: test
path: metaworld-faucet-close/test-*
- config_name: metaworld-faucet-open
data_files:
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path: metaworld-faucet-open/train-*
- split: test
path: metaworld-faucet-open/test-*
- config_name: metaworld-hammer
data_files:
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path: metaworld-hammer/train-*
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path: metaworld-hammer/test-*
- config_name: metaworld-hand-insert
data_files:
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path: metaworld-hand-insert/train-*
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path: metaworld-hand-insert/test-*
- config_name: metaworld-handle-press
data_files:
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path: metaworld-handle-press/train-*
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path: metaworld-handle-press/test-*
- config_name: metaworld-handle-press-side
data_files:
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path: metaworld-handle-press-side/train-*
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path: metaworld-handle-press-side/test-*
- config_name: metaworld-handle-pull
data_files:
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path: metaworld-handle-pull/train-*
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path: metaworld-handle-pull/test-*
- config_name: metaworld-handle-pull-side
data_files:
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path: metaworld-handle-pull-side/train-*
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path: metaworld-handle-pull-side/test-*
- config_name: metaworld-lever-pull
data_files:
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path: metaworld-lever-pull/train-*
- split: test
path: metaworld-lever-pull/test-*
- config_name: metaworld-peg-insert-side
data_files:
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path: metaworld-peg-insert-side/train-*
- split: test
path: metaworld-peg-insert-side/test-*
- config_name: metaworld-peg-unplug-side
data_files:
- split: train
path: metaworld-peg-unplug-side/train-*
- split: test
path: metaworld-peg-unplug-side/test-*
- config_name: metaworld-pick-out-of-hole
data_files:
- split: train
path: metaworld-pick-out-of-hole/train-*
- split: test
path: metaworld-pick-out-of-hole/test-*
- config_name: metaworld-pick-place
data_files:
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path: metaworld-pick-place/train-*
- split: test
path: metaworld-pick-place/test-*
- config_name: metaworld-pick-place-wall
data_files:
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path: metaworld-pick-place-wall/train-*
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path: metaworld-pick-place-wall/test-*
- config_name: metaworld-plate-slide
data_files:
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path: metaworld-plate-slide/train-*
- split: test
path: metaworld-plate-slide/test-*
- config_name: metaworld-plate-slide-back
data_files:
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path: metaworld-plate-slide-back/train-*
- split: test
path: metaworld-plate-slide-back/test-*
- config_name: metaworld-plate-slide-back-side
data_files:
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- split: test
path: metaworld-plate-slide-back-side/test-*
- config_name: metaworld-plate-slide-side
data_files:
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- config_name: metaworld-push
data_files:
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path: metaworld-push/test-*
- config_name: metaworld-push-back
data_files:
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path: metaworld-push-back/test-*
- config_name: metaworld-push-wall
data_files:
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path: metaworld-push-wall/train-*
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path: metaworld-push-wall/test-*
- config_name: metaworld-reach
data_files:
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path: metaworld-reach/train-*
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path: metaworld-reach/test-*
- config_name: metaworld-reach-wall
data_files:
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path: metaworld-reach-wall/train-*
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path: metaworld-reach-wall/test-*
- config_name: metaworld-shelf-place
data_files:
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path: metaworld-shelf-place/train-*
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path: metaworld-shelf-place/test-*
- config_name: metaworld-soccer
data_files:
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path: metaworld-soccer/train-*
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path: metaworld-soccer/test-*
- config_name: metaworld-stick-pull
data_files:
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- config_name: metaworld-stick-push
data_files:
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path: metaworld-stick-push/test-*
- config_name: metaworld-sweep
data_files:
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path: metaworld-sweep/train-*
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path: metaworld-sweep/test-*
- config_name: metaworld-sweep-into
data_files:
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path: metaworld-sweep-into/train-*
- split: test
path: metaworld-sweep-into/test-*
- config_name: metaworld-window-close
data_files:
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path: metaworld-window-close/train-*
- split: test
path: metaworld-window-close/test-*
- config_name: metaworld-window-open
data_files:
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path: metaworld-window-open/train-*
- split: test
path: metaworld-window-open/test-*
- config_name: mujoco-ant
data_files:
- split: train
path: mujoco-ant/train-*
- split: test
path: mujoco-ant/test-*
- config_name: mujoco-doublependulum
data_files:
- split: train
path: mujoco-doublependulum/train-*
- split: test
path: mujoco-doublependulum/test-*
- config_name: mujoco-halfcheetah
data_files:
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path: mujoco-halfcheetah/train-*
- split: test
path: mujoco-halfcheetah/test-*
- config_name: mujoco-hopper
data_files:
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path: mujoco-hopper/train-*
- split: test
path: mujoco-hopper/test-*
- config_name: mujoco-humanoid
data_files:
- split: train
path: mujoco-humanoid/train-*
- split: test
path: mujoco-humanoid/test-*
- config_name: mujoco-pendulum
data_files:
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path: mujoco-pendulum/train-*
- split: test
path: mujoco-pendulum/test-*
- config_name: mujoco-pusher
data_files:
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path: mujoco-pusher/train-*
- split: test
path: mujoco-pusher/test-*
- config_name: mujoco-reacher
data_files:
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path: mujoco-reacher/train-*
- split: test
path: mujoco-reacher/test-*
- config_name: mujoco-standup
data_files:
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path: mujoco-standup/train-*
- split: test
path: mujoco-standup/test-*
- config_name: mujoco-swimmer
data_files:
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path: mujoco-swimmer/train-*
- split: test
path: mujoco-swimmer/test-*
- config_name: mujoco-walker
data_files:
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path: mujoco-walker/train-*
- split: test
path: mujoco-walker/test-*
- config_name: ok-vqa
data_files:
- split: train
path: ok-vqa/train-*
- split: test
path: ok-vqa/test-*
- config_name: oscar
data_files:
- split: train
path: oscar/train-*
- split: test
path: oscar/test-*
- config_name: wikipedia
data_files:
- split: train
path: wikipedia/train-*
- split: test
path: wikipedia/test-*
tags:
- imitation-learning
- reinforcement-learning
- text-generation
- question-answering
- generalist-agent
dataset_info:
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---
# JAT Dataset
## Dataset Description
The Jack of All Trades (JAT) dataset combines a wide range of individual datasets. It includes expert demonstrations by expert RL agents, image and caption pairs, textual data and more. The JAT dataset is part of the JAT project, which aims to build a multimodal generalist agent.
**Paper**: https://huggingface.co/papers/2402.09844
### Usage
```python
>>> from datasets import load_dataset
>>> dataset = load_dataset("jat-project/jat-dataset", "metaworld-assembly")
>>> first_episode = dataset["train"][0]
>>> first_episode.keys()
dict_keys(['continuous_observations', 'continuous_actions', 'rewards'])
>>> len(first_episode["rewards"])
500
>>> first_episode["continuous_actions"][0]
[6.459120273590088, 2.2422609329223633, -5.914587020874023, -19.799840927124023]
```
## Dataset Structure
### Data Instances
<details>
<summary>Click to expand the score information for each task</summary>
The following table presents a comparative analysis of scores across various domains and tasks. The scores highlight the performance difference between a random agent and the episodes recorded in our dataset.
| Task | Random Agent Score | Dataset Episode Score |
| ----------------------------------- | :-----------------: | :-------------------: |
| **Atari** | | |
| atari-alien | 205.50 ± 111.97 | 16912.50 ± 7087.42 |
| atari-amidar | 2.38 ± 2.50 | 2164.71 ± 1229.47 |
| atari-assault | 262.50 ± 89.61 | 15699.12 ± 9572.12 |
| atari-asterix | 213.50 ± 110.87 | 3699.62 ± 2421.30 |
| atari-asteroids | 856.40 ± 434.32 | 177011.05 ± 35334.20 |
| atari-atlantis | 17764.00 ± 6662.43 | 320679.59 ± 418247.37 |
| atari-bankheist | 13.40 ± 11.07 | 1322.43 ± 60.84 |
| atari-battlezone | 2170.00 ± 2121.58 | 295592.59 ± 161960.96 |
| atari-beamrider | 357.28 ± 143.97 | 29589.35 ± 16132.96 |
| atari-berzerk | 160.10 ± 118.87 | 57085.26 ± 13104.53 |
| atari-bowling | 23.81 ± 6.07 | 20.40 ± 7.29 |
| atari-boxing | 0.52 ± 4.37 | 97.97 ± 3.77 |
| atari-breakout | 1.24 ± 1.30 | 702.97 ± 203.62 |
| atari-centipede | 2150.06 ± 1113.28 | 11624.29 ± 4918.34 |
| atari-choppercommand | 875.00 ± 416.98 | 90990.62 ± 270876.93 |
| atari-crazyclimber | 7376.00 ± 2253.09 | 179296.94 ± 39862.06 |
| atari-defender | 3417.50 ± 1443.41 | 351958.33 ± 40466.82 |
| atari-demonattack | 165.55 ± 92.93 | 92195.25 ± 26174.79 |
| atari-doubledunk | -18.54 ± 3.07 | 20.94 ± 3.65 |
| atari-enduro | 0.00 ± 0.00 | 2292.22 ± 147.54 |
| atari-fishingderby | -93.90 ± 3.51 | 7.18 ± 25.06 |
| atari-freeway | 0.01 ± 0.10 | 33.88 ± 0.35 |
| atari-frostbite | 67.60 ± 37.61 | 13196.12 ± 4341.00 |
| atari-gopher | 319.40 ± 228.24 | 81676.15 ± 46329.48 |
| atari-gravitar | 188.50 ± 203.33 | 3986.57 ± 1729.05 |
| atari-hero | 475.25 ± 894.95 | 44677.35 ± 1754.42 |
| atari-icehockey | -9.83 ± 3.24 | 25.17 ± 5.79 |
| atari-jamesbond | 28.50 ± 45.42 | 27786.89 ± 33819.20 |
| atari-kangaroo | 52.00 ± 108.15 | 574.05 ± 636.94 |
| atari-krull | 1754.00 ± 583.56 | 11439.83 ± 1218.34 |
| atari-kungfumaster | 390.00 ± 359.03 | 32392.81 ± 10006.55 |
| atari-montezumarevenge | 0.00 ± 0.00 | 393.53 ± 50.45 |
| atari-mspacman | 246.40 ± 121.22 | 6896.08 ± 2031.99 |
| atari-namethisgame | 2447.40 ± 888.97 | 22991.18 ± 2473.15 |
| atari-phoenix | 776.80 ± 635.86 | 424583.16 ± 97649.17 |
| atari-pitfall | -259.75 ± 384.26 | -1.45 ± 4.50 |
| atari-pong | -20.22 ± 0.95 | 20.99 ± 0.18 |
| atari-privateeye | 41.65 ± 191.83 | 100.00 ± 0.00 |
| atari-qbert | 164.25 ± 151.79 | 42971.37 ± 85070.72 |
| atari-riverraid | 1474.40 ± 314.59 | 14800.94 ± 7924.56 |
| atari-roadrunner | 11.00 ± 42.18 | 77942.80 ± 6088.62 |
| atari-robotank | 1.87 ± 1.59 | 80.51 ± 13.28 |
| atari-seaquest | 73.20 ± 57.91 | 2597.34 ± 386.09 |
| atari-skiing | -16299.52 ± 1850.70 | -10738.06 ± 111.13 |
| atari-solaris | 2360.40 ± 1852.03 | 1353.68 ± 516.96 |
| atari-spaceinvaders | 137.20 ± 95.82 | 29425.29 ± 23623.89 |
| atari-stargunner | 652.00 ± 312.24 | 360588.57 ± 49207.71 |
| atari-surround | -9.99 ± 0.10 | 9.39 ± 0.85 |
| atari-tennis | -23.95 ± 0.22 | 11.11 ± 7.57 |
| atari-timepilot | 3396.00 ± 2128.85 | 69583.33 ± 29838.67 |
| atari-tutankham | 12.73 ± 17.40 | 291.16 ± 30.37 |
| atari-upndown | 358.90 ± 380.11 | 429418.33 ± 7187.43 |
| atari-venture | 0.00 ± 0.00 | 0.00 ± 0.00 |
| atari-videopinball | 23917.17 ± 19449.59 | 441507.92 ± 283264.62 |
| atari-wizardofwor | 620.00 ± 837.85 | 49333.33 ± 16157.08 |
| atari-yarsrevenge | 3503.91 ± 906.14 | 270262.86 ± 161815.96 |
| atari-zaxxon | 21.00 ± 102.27 | 73097.22 ± 14825.77 |
| **BabyAI** | | |
| babyai-action-obj-door | 0.37 ± 0.39 | 0.99 ± 0.01 |
| babyai-blocked-unlock-pickup | 0.00 ± 0.02 | 0.95 ± 0.01 |
| babyai-boss-level | 0.06 ± 0.21 | 0.94 ± 0.05 |
| babyai-boss-level-no-unlock | 0.06 ± 0.19 | 0.94 ± 0.05 |
| babyai-find-obj-s5 | 0.08 ± 0.23 | 0.95 ± 0.04 |
| babyai-go-to | 0.13 ± 0.29 | 0.92 ± 0.07 |
| babyai-go-to-door | 0.45 ± 0.38 | 0.99 ± 0.00 |
| babyai-go-to-imp-unlock | 0.08 ± 0.23 | 0.83 ± 0.13 |
| babyai-go-to-local | 0.16 ± 0.30 | 0.93 ± 0.04 |
| babyai-go-to-obj | 0.13 ± 0.27 | 0.93 ± 0.03 |
| babyai-go-to-obj-door | 0.53 ± 0.39 | 0.99 ± 0.01 |
| babyai-go-to-red-ball | 0.17 ± 0.30 | 0.93 ± 0.04 |
| babyai-go-to-red-ball-grey | 0.12 ± 0.27 | 0.92 ± 0.05 |
| babyai-go-to-red-ball-no-dists | 0.14 ± 0.28 | 0.93 ± 0.03 |
| babyai-go-to-red-blue-ball | 0.12 ± 0.27 | 0.92 ± 0.05 |
| babyai-go-to-seq | 0.08 ± 0.23 | 0.94 ± 0.05 |
| babyai-key-corridor | 0.00 ± 0.00 | 0.91 ± 0.01 |
| babyai-mini-boss-level | 0.07 ± 0.21 | 0.89 ± 0.10 |
| babyai-move-two-across-s8n9 | 0.00 ± 0.00 | 0.96 ± 0.01 |
| babyai-one-room-s8 | 0.08 ± 0.21 | 0.92 ± 0.03 |
| babyai-open | 0.10 ± 0.24 | 0.95 ± 0.05 |
| babyai-open-door | 0.23 ± 0.34 | 0.99 ± 0.00 |
| babyai-open-doors-order-n4 | 0.16 ± 0.30 | 0.99 ± 0.01 |
| babyai-open-red-door | 0.08 ± 0.21 | 0.92 ± 0.03 |
| babyai-open-two-doors | 0.08 ± 0.20 | 0.98 ± 0.00 |
| babyai-pickup | 0.08 ± 0.22 | 0.92 ± 0.07 |
| babyai-pickup-above | 0.02 ± 0.09 | 0.91 ± 0.07 |
| babyai-pickup-dist | 0.10 ± 0.24 | 0.86 ± 0.21 |
| babyai-pickup-loc | 0.08 ± 0.23 | 0.91 ± 0.04 |
| babyai-put-next | 0.00 ± 0.03 | 0.96 ± 0.01 |
| babyai-put-next-local | 0.00 ± 0.05 | 0.92 ± 0.03 |
| babyai-synth | 0.11 ± 0.26 | 0.93 ± 0.06 |
| babyai-synth-loc | 0.13 ± 0.29 | 0.94 ± 0.06 |
| babyai-synth-seq | 0.07 ± 0.20 | 0.95 ± 0.04 |
| babyai-unblock-pickup | 0.08 ± 0.22 | 0.91 ± 0.08 |
| babyai-unlock | 0.03 ± 0.15 | 0.87 ± 0.10 |
| babyai-unlock-local | 0.01 ± 0.09 | 0.98 ± 0.01 |
| babyai-unlock-pickup | 0.00 ± 0.00 | 0.75 ± 0.04 |
| babyai-unlock-to-unlock | 0.00 ± 0.00 | 0.96 ± 0.00 |
| **Meta-World** | | |
| metaworld-assembly | 45.30 ± 4.13 | 245.99 ± 3.50 |
| metaworld-basketball | 2.81 ± 1.24 | 627.99 ± 1.98 |
| metaworld-bin-picking | 1.89 ± 0.45 | 425.58 ± 101.86 |
| metaworld-box-close | 76.39 ± 17.91 | 512.49 ± 107.81 |
| metaworld-button-press | 31.73 ± 5.20 | 643.10 ± 12.85 |
| metaworld-button-press-topdown | 28.97 ± 10.37 | 490.18 ± 27.21 |
| metaworld-button-press-topdown-wall | 29.04 ± 10.52 | 497.19 ± 31.37 |
| metaworld-button-press-wall | 8.98 ± 3.99 | 675.41 ± 15.04 |
| metaworld-coffee-button | 31.72 ± 6.36 | 731.08 ± 29.34 |
| metaworld-coffee-pull | 4.09 ± 0.38 | 259.86 ± 88.48 |
| metaworld-coffee-push | 4.17 ± 0.76 | 496.78 ± 118.20 |
| metaworld-dial-turn | 29.64 ± 16.67 | 793.56 ± 80.06 |
| metaworld-disassemble | 40.31 ± 7.53 | 42.83 ± 6.30 |
| metaworld-door-close | 5.30 ± 1.33 | 529.75 ± 27.24 |
| metaworld-door-lock | 112.35 ± 28.63 | 811.52 ± 34.07 |
| metaworld-door-open | 56.37 ± 11.23 | 581.94 ± 19.67 |
| metaworld-door-unlock | 94.17 ± 15.56 | 802.88 ± 17.05 |
| metaworld-drawer-close | 116.73 ± 253.11 | 867.92 ± 4.48 |
| metaworld-drawer-open | 126.85 ± 25.22 | 492.99 ± 2.52 |
| metaworld-faucet-close | 253.12 ± 22.94 | 753.92 ± 13.42 |
| metaworld-faucet-open | 244.10 ± 23.25 | 705.76 ± 7.15 |
| metaworld-hammer | 95.33 ± 9.02 | 693.17 ± 34.62 |
| metaworld-hand-insert | 2.75 ± 3.53 | 740.53 ± 36.69 |
| metaworld-handle-press | 80.41 ± 110.19 | 855.91 ± 72.75 |
| metaworld-handle-press-side | 57.00 ± 39.47 | 861.12 ± 20.01 |
| metaworld-handle-pull | 10.34 ± 13.54 | 669.35 ± 24.81 |
| metaworld-handle-pull-side | 2.13 ± 2.76 | 384.65 ± 102.89 |
| metaworld-lever-pull | 60.31 ± 15.77 | 612.04 ± 38.85 |
| metaworld-peg-insert-side | 1.71 ± 0.36 | 315.23 ± 140.07 |
| metaworld-peg-unplug-side | 4.75 ± 2.83 | 456.12 ± 81.65 |
| metaworld-pick-out-of-hole | 1.51 ± 0.24 | 219.61 ± 88.85 |
| metaworld-pick-place | 1.61 ± 0.99 | 419.10 ± 98.19 |
| metaworld-pick-place-wall | 0.00 ± 0.01 | 450.57 ± 64.10 |
| metaworld-plate-slide | 74.64 ± 13.84 | 527.01 ± 155.34 |
| metaworld-plate-slide-back | 33.47 ± 11.22 | 718.22 ± 87.41 |
| metaworld-plate-slide-back-side | 34.34 ± 11.53 | 729.61 ± 69.15 |
| metaworld-plate-slide-side | 22.61 ± 17.36 | 662.81 ± 102.81 |
| metaworld-push | 5.51 ± 2.43 | 750.57 ± 43.98 |
| metaworld-push-back | 1.21 ± 0.16 | 85.05 ± 107.12 |
| metaworld-push-wall | 6.13 ± 3.17 | 748.87 ± 10.62 |
| metaworld-reach | 149.67 ± 44.70 | 681.37 ± 133.68 |
| metaworld-reach-wall | 143.26 ± 36.56 | 746.12 ± 104.19 |
| metaworld-shelf-place | 0.00 ± 0.01 | 241.34 ± 24.60 |
| metaworld-soccer | 5.66 ± 4.61 | 375.15 ± 140.24 |
| metaworld-stick-pull | 2.64 ± 1.41 | 523.55 ± 18.94 |
| metaworld-stick-push | 2.81 ± 1.04 | 627.95 ± 10.20 |
| metaworld-sweep | 11.23 ± 7.28 | 494.85 ± 43.29 |
| metaworld-sweep-into | 12.55 ± 10.72 | 799.21 ± 19.07 |
| metaworld-window-close | 57.46 ± 7.11 | 591.30 ± 38.63 |
| metaworld-window-open | 43.36 ± 2.09 | 590.82 ± 57.08 |
| **MuJoCo** | | |
| mujoco-ant | -59.95 ± 99.62 | 5846.42 ± 942.55 |
| mujoco-doublependulum | 57.46 ± 17.54 | 9338.69 ± 352.61 |
| mujoco-halfcheetah | -284.97 ± 79.83 | 7437.77 ± 173.30 |
| mujoco-hopper | 18.38 ± 17.09 | 1858.73 ± 534.07 |
| mujoco-humanoid | 122.02 ± 35.28 | 6281.02 ± 1795.84 |
| mujoco-pendulum | 6.07 ± 3.47 | 475.40 ± 178.96 |
| mujoco-pusher | -149.69 ± 7.41 | -25.21 ± 6.66 |
| mujoco-reacher | -43.00 ± 3.91 | -5.68 ± 2.53 |
| mujoco-standup | 33135.75 ± 2481.89 | 273574.16 ± 85253.26 |
| mujoco-swimmer | 0.80 ± 10.71 | 92.18 ± 4.44 |
| mujoco-walker | 2.68 ± 6.06 | 4631.22 ± 1059.01 |
</details>
### Data Fields
- `text`: a `string` feature
- `images`: a `image` feature
- `image_observations` : a `Sequence(image)` feature
- `text_observations` : a `Sequence(string)` feature
- `discrete_observations`: a `Sequence(Sequence(int64))` feature
- `continuous_observations`: a `Sequence(Sequence(float32))` feature
- `continuous_actions`: a `Sequence(Sequence(float32))` feature
- `discrete_actions`: a `Sequence(int64)` feature
- `rewards`: a `Sequence(float32)` feature
### Data Splits
- `train`: `` examples
- `test`: `` examples
## Dataset Creation
This section describes how our dataset was created. We specifically detail how data for each domain and task were generated. The generation scripts are available in the [JAT repository](https://github.com/huggingface/jat). For RL tasks, we trained one agent per task using the [Sample Factory](https://www.samplefactory.dev). Then we used the trained agent to generate episodes.
### Atari
We used the 57 [ALE/Atari](https://github.com/Farama-Foundation/Arcade-Learning-Environment) games as our environment, configuring the following parameters for our experiments. We rendered the images in grayscale with an 84x84 pixel resolution. The agent interacted with the environment every 4 frames. Sticky actions were not used, and the raw reward (no clipping) was reported. Episodes were stored as complete, i.e. with no termination on life loss.
### BabyAI
We used BabyAI's implementation from [Minigrid](https://github.com/Farama-Foundation/Minigrid).
We reused the [bot agent](https://github.com/mila-iqia/babyai) provided with BabyAI's paper and adapted it to the new Minigrid API.
Using the bot, we generated 1.000.000 interractions for each of the 39 tasks of [Minigrid's BabyAI](https://minigrid.farama.org/environments/babyai/) and stored for each step:
- the mission: str
- the concatenation of the symbolic observation flattened and the direction: Array of integers of size (147,)
- the action: integer
- the reward: float
### Conceptual Captions
The [Conceptual Captions](https://github.com/google-research-datasets/conceptual-captions/tree/master) dataset, offered by Google LLC, comprises pairs of image links and their corresponding captions. Each image has been downloaded and, when required, resized to ensure the maximum dimension does not exceed 352 pixels.
### Meta-World
We used the 50 tasks from [Meta-World v2](https://github.com/Farama-Foundation/Metaworld). We constrained the episode to a duration of 100 timesteps, which is always sufficient to solve the task.
### MuJoCo
We used the 11 environments of Gymnasium MuJoCo.
### OK-VQA
The [OK-VQA](https://okvqa.allenai.org/index.html) dataset released by Kenneth Marino, Mohammad Rastegari, Ali Farhadi, Roozbeh Mottaghi was used.
The data were formatted to match Hugging Face dataset's requirements and images were resized such that the largest dimension is at most 352.
### OSCAR
We modified the "unshuffled_deduplicated_en" split of [OSCAR 2019](https://huggingface.co/datasets/oscar) dataset, initially put together by Pedro J. Ortiz, Benoît Sagot, and Laurent Romary and licensed under [CC BY 4.0](https://oscar-project.github.io/documentation/versions/oscar-2019/#license).
We cleaned and deduplicated the dataset using [the methods](https://github.com/bigscience-workshop/data-preparation/tree/main/preprocessing/training/01b_oscar_cleaning_and_filtering) and parameters used for the [ROOTS dataset](https://arxiv.org/abs/2303.03915) (Lurençon et al., 2023).
The dataset was splitted into 30 even shards each cleaned and deduplicated independently before being concatenated again.
### Wikipedia
We used the english version of the [Wikipedia dataset](https://huggingface.co/datasets/wikipedia).
## Considerations for Using the Data
### Known Issues
- Some BabyAI tasks are missing due to incompatibility with the training bot:
- `babyai-key-in-box`
- `babyai-go-to-imp-unlock`
- `babyai-unlock-to-unlock`
- `babyai-unlock`
- For some atari tasks, the episode is too long, causing an `OverflowError` when loading the dataset:
- `atari-enduro`
- For some tasks, although the score can be higher than the random agent, we can't consider the task as solved:
- `atari-bowling`
- `atari-privateeye`
- `atari-solaris`
- `atari-venture`
- `metaworld-bin-picking`
- `metaworld-disassemble`
- `metaworld-peg-insert-side`
- `metaworld-plate-slide`
- `metaworld-push-back`
### Future Developments
We plan to expand the dataset to include the following additional domains:
- [ ] DM Lab
- [ ] Sokoban
- [ ] Procgen
- [ ] DM Control Suite (w and w/o pixels)
## Additional Information
### Licensing Information
This dataset is release under the Apache 2.0 license.
### Citation Information
```bibtex
@article{gallouedec2024jack,
title = {{Jack of All Trades, Master of Some: a Multi-Purpose Transformer Agent}},
author = {Gallouédec, Quentin and Beeching, Edward and Romac, Clément and Dellandréa, Emmanuel},
journal = {arXiv preprint arXiv:2402.09844},
year = {2024},
url = {https://arxiv.org/abs/2402.09844}
}
```
## Acknowledgment
We would like to extend our sincere gratitude to:
- [Shengyi Costa Huang](https://huggingface.co/vwxyzjn) for his invaluable assistance with the pretrained models used in this research |
sebdg/trading_data | ---
license: apache-2.0
task_categories:
- time-series-forecasting
tags:
- finance
- crypto
- stocks
- funds
dataset_info:
features:
- name: Open
dtype: float64
- name: High
dtype: float64
- name: Low
dtype: float64
- name: Close
dtype: float64
- name: Adj Close
dtype: float64
- name: Volume
dtype: float64
- name: File
dtype: string
- name: Date
dtype: string
splits:
- name: train
num_bytes: 1954176878
num_examples: 28151758
download_size: 931065124
dataset_size: 1954176878
configs:
- config_name: all
default: true
data_files:
- split: train
path: data/*
- config_name: etfs
data_files:
- split: train
path: data/etfs/*
- config_name: stocks
data_files:
- split: train
path: data/stocks/*
---
|
Gizachew/amh-ner | ---
dataset_info:
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-PER
'2': I-PER
'3': B-ORG
'4': I-ORG
'5': B-LOC
'6': I-LOC
'7': B-DATE
'8': I-DATE
splits:
- name: train
num_bytes: 639427
num_examples: 1750
- name: validation
num_bytes: 92479
num_examples: 250
- name: test
num_bytes: 184216
num_examples: 500
download_size: 269989
dataset_size: 916122
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
autoevaluate/autoeval-eval-social_i_qa-default-cabb3b-37040145035 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- social_i_qa
eval_info:
task: extractive_question_answering
model: 96harsh56/bert_test1
metrics: []
dataset_name: social_i_qa
dataset_config: default
dataset_split: train
col_mapping:
context: context
question: question
answers-text: answerA
answers-answer_start: 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: Question Answering
* Model: 96harsh56/bert_test1
* Dataset: social_i_qa
* Config: default
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@kingmbc](https://huggingface.co/kingmbc) for evaluating this model. |
argilla/ultrafeedback-multi-binarized-quality-preferences-cleaned | ---
dataset_info:
features:
- name: source
dtype: string
- name: prompt
dtype: string
- name: chosen
list:
- name: content
dtype: string
- name: role
dtype: string
- name: chosen-rating
dtype: float64
- name: chosen-model
dtype: string
- name: rejected
list:
- name: content
dtype: string
- name: role
dtype: string
- name: rejected-rating
dtype: float64
- name: rejected-model
dtype: string
splits:
- name: train
num_bytes: 724022562.4845791
num_examples: 154663
download_size: 194977204
dataset_size: 724022562.4845791
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
itacasehold/itacasehold | ---
license: apache-2.0
dataset_info:
features:
- name: url
dtype: string
- name: title
dtype: string
- name: doc
dtype: string
- name: summary
dtype: string
- name: materia
dtype: string
splits:
- name: train
num_bytes: 25541563
num_examples: 792
- name: validation
num_bytes: 2932410
num_examples: 88
- name: test
num_bytes: 6870636
num_examples: 221
download_size: 18051772
dataset_size: 35344609
task_categories:
- summarization
- text-classification
language:
- it
tags:
- legal
pretty_name: ita_casehold
size_categories:
- n<1K
---
# ITA-CASEHOLD
## Dataset Summary
- This dataset contains the data used in the research of the ITA-CASEHOLD model, an extractive summarization model to extract holdings from Italian Legal Administrative documents.
- The research paper titled 'Legal Holding Extraction from Italian Case Documents using Italian-LEGAL-BERT Text Summarization' is accepted for ICAIL 23.
- It consists of 1101 pairs of judgments and their official holdings between the years 2019 and 2022 from the archives of [Italian Administrative Justice](https://www.giustizia-amministrativa.it/it/web/guest/massime).
- The Administrative Justice system in Italy covers a wide range of issues, including public contracts, environmental protection, public services, immigration, taxes, and compensation for damages caused by the State
### Download the dataset
To download the dataset, use the following lines:
from datasets import load_dataset
dataset = load_dataset("itacasehold/itacasehold")
To split the train, test, and validation dataset, use
dataset = load_dataset("itacasehold/itacasehold", split = 'train')
### Supported Tasks and Leaderboards
Summarization, Multi-class Text classification
### Languages
Italian
### Data Fields
The dataset consists of
- **URL**: link to the document
- **Document**: The document
- **Summary**: The holding of the document
- **Materia** : Legal subject
- **Title** : Title of the document
### Data Splits
- **Train** : 792
- **Validatio** : 88
- **Test** : 221
### Source Data
The data is collected from ['Judicial Administration site'](https://www.giustizia-amministrativa.it/it/web/guest/massime).
### Social Impact of Dataset
Legal holdings are considered the most essential part of a legal decision because they summarize it without going into the merits of the specific case, establish a legal principle and set a legal precedent.
The holdings writing is carried out by legal experts who, starting from a judgment, set out the applied principle of law in a clear, precise, and concise manner.
We approached the problem of extracting legal holdings as an Extractive text summarization task.
This Dataset addresses the Legal holding Extraction topic and so far the first and the only one present in the Italian language.
This dataset contributes to Summarization in the Italian language and Summarization tasks in Legal domains.
Apart from this, the Dataset can also be used as a multi-class text classification task utilizing legal subjects.
### Dataset Limitation
This Dataset specifically focuses on the Italian Legal domain, and it is only in Italian. The documents are only from the period of 2019-2022.
## Additional Information
### Dataset Curators
The Dataset was curated by researchers from Scoula Superiore Sant'Anna as a part of the project ['Guistizia Agile (Agile Justice)'](https://www.unitus.it/it/unitus/mappatura-della-ricerca/articolo/giustizia-agile) funded by the Italian Ministry of Justice.
### Licensing Information
The data sets are distributed under the `Apache 2.0` License. More information about the terms of use of the original data sets is listed [here](https://www.apache.org/licenses/LICENSE-2.0).
### Citation Information
If you use this dataset then, please, cite the following paper:
@inproceedings{10.1145/3594536.3595177,
author = {Licari, Daniele and Bushipaka, Praveen and Marino, Gabriele and Comand\'{e}, Giovanni and Cucinotta, Tommaso},
title = {Legal Holding Extraction from Italian Case Documents using Italian-LEGAL-BERT Text Summarization},
year = {2023},
isbn = {9798400701979},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3594536.3595177},
doi = {10.1145/3594536.3595177},
abstract = {Legal holdings are used in Italy as a critical component of the legal system, serving to establish legal precedents, provide guidance for future legal decisions, and ensure consistency and predictability in the interpretation and application of the law. They are written by domain experts who describe in a clear and concise manner the principle of law applied in the judgments.We introduce a legal holding extraction method based on Italian-LEGAL-BERT to automatically extract legal holdings from Italian cases. In addition, we present ITA-CaseHold, a benchmark dataset for Italian legal summarization. We conducted several experiments using this dataset, as a valuable baseline for future research on this topic.},
booktitle = {Proceedings of the Nineteenth International Conference on Artificial Intelligence and Law},
pages = {148–156},
numpages = {9},
keywords = {Italian-LEGAL-BERT, Holding Extraction, Extractive Text Summarization, Benchmark Dataset},
location = {<conf-loc>, <city>Braga</city>, <country>Portugal</country>, </conf-loc>},
series = {ICAIL '23}
}
|
open-llm-leaderboard/details_rishiraj__oswald-2x7b | ---
pretty_name: Evaluation run of rishiraj/oswald-2x7b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [rishiraj/oswald-2x7b](https://huggingface.co/rishiraj/oswald-2x7b) 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_rishiraj__oswald-2x7b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-01-11T10:06:31.070515](https://huggingface.co/datasets/open-llm-leaderboard/details_rishiraj__oswald-2x7b/blob/main/results_2024-01-11T10-06-31.070515.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\": 0.6533236478227353,\n\
\ \"acc_stderr\": 0.03188761134034235,\n \"acc_norm\": 0.6556283671019292,\n\
\ \"acc_norm_stderr\": 0.032521516691180946,\n \"mc1\": 0.4357405140758874,\n\
\ \"mc1_stderr\": 0.017358345398863124,\n \"mc2\": 0.6006314442487943,\n\
\ \"mc2_stderr\": 0.015414089468190334\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6279863481228669,\n \"acc_stderr\": 0.01412459788184446,\n\
\ \"acc_norm\": 0.6646757679180887,\n \"acc_norm_stderr\": 0.013796182947785562\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6697868950408286,\n\
\ \"acc_stderr\": 0.004693285694663836,\n \"acc_norm\": 0.8546106353316073,\n\
\ \"acc_norm_stderr\": 0.003517725787017748\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695236,\n \
\ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695236\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6148148148148148,\n\
\ \"acc_stderr\": 0.04203921040156279,\n \"acc_norm\": 0.6148148148148148,\n\
\ \"acc_norm_stderr\": 0.04203921040156279\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.7236842105263158,\n \"acc_stderr\": 0.03639057569952928,\n\
\ \"acc_norm\": 0.7236842105263158,\n \"acc_norm_stderr\": 0.03639057569952928\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.63,\n\
\ \"acc_stderr\": 0.04852365870939099,\n \"acc_norm\": 0.63,\n \
\ \"acc_norm_stderr\": 0.04852365870939099\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7132075471698113,\n \"acc_stderr\": 0.027834912527544067,\n\
\ \"acc_norm\": 0.7132075471698113,\n \"acc_norm_stderr\": 0.027834912527544067\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7569444444444444,\n\
\ \"acc_stderr\": 0.035868792800803406,\n \"acc_norm\": 0.7569444444444444,\n\
\ \"acc_norm_stderr\": 0.035868792800803406\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.47,\n \"acc_stderr\": 0.050161355804659205,\n \
\ \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.050161355804659205\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.57,\n \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\"\
: 0.57,\n \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.33,\n \"acc_stderr\": 0.047258156262526045,\n \
\ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.047258156262526045\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.653179190751445,\n\
\ \"acc_stderr\": 0.036291466701596636,\n \"acc_norm\": 0.653179190751445,\n\
\ \"acc_norm_stderr\": 0.036291466701596636\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\":\
\ 0.78,\n \"acc_stderr\": 0.04163331998932263,\n \"acc_norm\": 0.78,\n\
\ \"acc_norm_stderr\": 0.04163331998932263\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5574468085106383,\n \"acc_stderr\": 0.032469569197899575,\n\
\ \"acc_norm\": 0.5574468085106383,\n \"acc_norm_stderr\": 0.032469569197899575\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5175438596491229,\n\
\ \"acc_stderr\": 0.04700708033551038,\n \"acc_norm\": 0.5175438596491229,\n\
\ \"acc_norm_stderr\": 0.04700708033551038\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5448275862068965,\n \"acc_stderr\": 0.04149886942192117,\n\
\ \"acc_norm\": 0.5448275862068965,\n \"acc_norm_stderr\": 0.04149886942192117\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.43915343915343913,\n \"acc_stderr\": 0.025559920550531,\n \"acc_norm\"\
: 0.43915343915343913,\n \"acc_norm_stderr\": 0.025559920550531\n },\n\
\ \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4523809523809524,\n\
\ \"acc_stderr\": 0.044518079590553275,\n \"acc_norm\": 0.4523809523809524,\n\
\ \"acc_norm_stderr\": 0.044518079590553275\n },\n \"harness|hendrycksTest-global_facts|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-high_school_biology|5\": {\n \"acc\": 0.7903225806451613,\n\
\ \"acc_stderr\": 0.023157879349083522,\n \"acc_norm\": 0.7903225806451613,\n\
\ \"acc_norm_stderr\": 0.023157879349083522\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.5073891625615764,\n \"acc_stderr\": 0.035176035403610105,\n\
\ \"acc_norm\": 0.5073891625615764,\n \"acc_norm_stderr\": 0.035176035403610105\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\": 0.7878787878787878,\n \"acc_stderr\": 0.03192271569548301,\n\
\ \"acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.03192271569548301\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7777777777777778,\n \"acc_stderr\": 0.029620227874790482,\n \"\
acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.029620227874790482\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\": 0.6615384615384615,\n \"acc_stderr\": 0.023991500500313036,\n\
\ \"acc_norm\": 0.6615384615384615,\n \"acc_norm_stderr\": 0.023991500500313036\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.3037037037037037,\n \"acc_stderr\": 0.028037929969115,\n \
\ \"acc_norm\": 0.3037037037037037,\n \"acc_norm_stderr\": 0.028037929969115\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.7058823529411765,\n \"acc_stderr\": 0.029597329730978086,\n\
\ \"acc_norm\": 0.7058823529411765,\n \"acc_norm_stderr\": 0.029597329730978086\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.3841059602649007,\n \"acc_stderr\": 0.03971301814719197,\n \"\
acc_norm\": 0.3841059602649007,\n \"acc_norm_stderr\": 0.03971301814719197\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8385321100917431,\n \"acc_stderr\": 0.015776239256163255,\n \"\
acc_norm\": 0.8385321100917431,\n \"acc_norm_stderr\": 0.015776239256163255\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5046296296296297,\n \"acc_stderr\": 0.03409825519163572,\n \"\
acc_norm\": 0.5046296296296297,\n \"acc_norm_stderr\": 0.03409825519163572\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8431372549019608,\n \"acc_stderr\": 0.02552472232455334,\n \"\
acc_norm\": 0.8431372549019608,\n \"acc_norm_stderr\": 0.02552472232455334\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.810126582278481,\n \"acc_stderr\": 0.02553010046023349,\n \
\ \"acc_norm\": 0.810126582278481,\n \"acc_norm_stderr\": 0.02553010046023349\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7040358744394619,\n\
\ \"acc_stderr\": 0.03063659134869981,\n \"acc_norm\": 0.7040358744394619,\n\
\ \"acc_norm_stderr\": 0.03063659134869981\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7862595419847328,\n \"acc_stderr\": 0.0359546161177469,\n\
\ \"acc_norm\": 0.7862595419847328,\n \"acc_norm_stderr\": 0.0359546161177469\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.8181818181818182,\n \"acc_stderr\": 0.03520893951097654,\n \"\
acc_norm\": 0.8181818181818182,\n \"acc_norm_stderr\": 0.03520893951097654\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8055555555555556,\n\
\ \"acc_stderr\": 0.038260763248848646,\n \"acc_norm\": 0.8055555555555556,\n\
\ \"acc_norm_stderr\": 0.038260763248848646\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7975460122699386,\n \"acc_stderr\": 0.031570650789119005,\n\
\ \"acc_norm\": 0.7975460122699386,\n \"acc_norm_stderr\": 0.031570650789119005\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\": 0.8058252427184466,\n \"acc_stderr\": 0.03916667762822584,\n\
\ \"acc_norm\": 0.8058252427184466,\n \"acc_norm_stderr\": 0.03916667762822584\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8717948717948718,\n\
\ \"acc_stderr\": 0.021901905115073325,\n \"acc_norm\": 0.8717948717948718,\n\
\ \"acc_norm_stderr\": 0.021901905115073325\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.74,\n \"acc_stderr\": 0.04408440022768079,\n \
\ \"acc_norm\": 0.74,\n \"acc_norm_stderr\": 0.04408440022768079\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8326947637292464,\n\
\ \"acc_stderr\": 0.013347327202920332,\n \"acc_norm\": 0.8326947637292464,\n\
\ \"acc_norm_stderr\": 0.013347327202920332\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7456647398843931,\n \"acc_stderr\": 0.023445826276545543,\n\
\ \"acc_norm\": 0.7456647398843931,\n \"acc_norm_stderr\": 0.023445826276545543\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3005586592178771,\n\
\ \"acc_stderr\": 0.01533456680625116,\n \"acc_norm\": 0.3005586592178771,\n\
\ \"acc_norm_stderr\": 0.01533456680625116\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7418300653594772,\n \"acc_stderr\": 0.02505850331695814,\n\
\ \"acc_norm\": 0.7418300653594772,\n \"acc_norm_stderr\": 0.02505850331695814\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7106109324758842,\n\
\ \"acc_stderr\": 0.025755865922632945,\n \"acc_norm\": 0.7106109324758842,\n\
\ \"acc_norm_stderr\": 0.025755865922632945\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7469135802469136,\n \"acc_stderr\": 0.024191808600713,\n\
\ \"acc_norm\": 0.7469135802469136,\n \"acc_norm_stderr\": 0.024191808600713\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.49645390070921985,\n \"acc_stderr\": 0.02982674915328092,\n \
\ \"acc_norm\": 0.49645390070921985,\n \"acc_norm_stderr\": 0.02982674915328092\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.47783572359843546,\n\
\ \"acc_stderr\": 0.012757683047716177,\n \"acc_norm\": 0.47783572359843546,\n\
\ \"acc_norm_stderr\": 0.012757683047716177\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6691176470588235,\n \"acc_stderr\": 0.02858270975389845,\n\
\ \"acc_norm\": 0.6691176470588235,\n \"acc_norm_stderr\": 0.02858270975389845\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6797385620915033,\n \"acc_stderr\": 0.018875682938069443,\n \
\ \"acc_norm\": 0.6797385620915033,\n \"acc_norm_stderr\": 0.018875682938069443\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6818181818181818,\n\
\ \"acc_stderr\": 0.04461272175910509,\n \"acc_norm\": 0.6818181818181818,\n\
\ \"acc_norm_stderr\": 0.04461272175910509\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7428571428571429,\n \"acc_stderr\": 0.027979823538744543,\n\
\ \"acc_norm\": 0.7428571428571429,\n \"acc_norm_stderr\": 0.027979823538744543\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.845771144278607,\n\
\ \"acc_stderr\": 0.025538433368578337,\n \"acc_norm\": 0.845771144278607,\n\
\ \"acc_norm_stderr\": 0.025538433368578337\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.85,\n \"acc_stderr\": 0.0358870281282637,\n \
\ \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.0358870281282637\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\": 0.8362573099415205,\n \"acc_stderr\": 0.028380919596145866,\n\
\ \"acc_norm\": 0.8362573099415205,\n \"acc_norm_stderr\": 0.028380919596145866\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.4357405140758874,\n\
\ \"mc1_stderr\": 0.017358345398863124,\n \"mc2\": 0.6006314442487943,\n\
\ \"mc2_stderr\": 0.015414089468190334\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7940015785319653,\n \"acc_stderr\": 0.011366474352008826\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.5981804397270659,\n \
\ \"acc_stderr\": 0.013504357787494042\n }\n}\n```"
repo_url: https://huggingface.co/rishiraj/oswald-2x7b
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_01_11T10_06_31.070515
path:
- '**/details_harness|arc:challenge|25_2024-01-11T10-06-31.070515.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-01-11T10-06-31.070515.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_01_11T10_06_31.070515
path:
- '**/details_harness|gsm8k|5_2024-01-11T10-06-31.070515.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-01-11T10-06-31.070515.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_01_11T10_06_31.070515
path:
- '**/details_harness|hellaswag|10_2024-01-11T10-06-31.070515.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-01-11T10-06-31.070515.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_01_11T10_06_31.070515
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-11T10-06-31.070515.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-11T10-06-31.070515.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-11T10-06-31.070515.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_01_11T10_06_31.070515
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-11T10-06-31.070515.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-11T10-06-31.070515.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_01_11T10_06_31.070515
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-11T10-06-31.070515.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-11T10-06-31.070515.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_01_11T10_06_31.070515
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-11T10-06-31.070515.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-11T10-06-31.070515.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_01_11T10_06_31.070515
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-11T10-06-31.070515.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-11T10-06-31.070515.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_01_11T10_06_31.070515
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-11T10-06-31.070515.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-11T10-06-31.070515.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_01_11T10_06_31.070515
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-11T10-06-31.070515.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-11T10-06-31.070515.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_01_11T10_06_31.070515
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-11T10-06-31.070515.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-11T10-06-31.070515.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_01_11T10_06_31.070515
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-11T10-06-31.070515.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-11T10-06-31.070515.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_01_11T10_06_31.070515
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-11T10-06-31.070515.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-11T10-06-31.070515.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_01_11T10_06_31.070515
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-11T10-06-31.070515.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-11T10-06-31.070515.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_01_11T10_06_31.070515
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-11T10-06-31.070515.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-11T10-06-31.070515.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_01_11T10_06_31.070515
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-11T10-06-31.070515.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-11T10-06-31.070515.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_01_11T10_06_31.070515
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-11T10-06-31.070515.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-11T10-06-31.070515.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_01_11T10_06_31.070515
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-11T10-06-31.070515.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-11T10-06-31.070515.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_01_11T10_06_31.070515
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-11T10-06-31.070515.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-11T10-06-31.070515.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_01_11T10_06_31.070515
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-11T10-06-31.070515.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-11T10-06-31.070515.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_01_11T10_06_31.070515
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-11T10-06-31.070515.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-11T10-06-31.070515.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_01_11T10_06_31.070515
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-11T10-06-31.070515.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-11T10-06-31.070515.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_01_11T10_06_31.070515
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-11T10-06-31.070515.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-11T10-06-31.070515.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_01_11T10_06_31.070515
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-11T10-06-31.070515.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-11T10-06-31.070515.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_01_11T10_06_31.070515
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-11T10-06-31.070515.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-11T10-06-31.070515.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_01_11T10_06_31.070515
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-11T10-06-31.070515.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-11T10-06-31.070515.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_01_11T10_06_31.070515
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-11T10-06-31.070515.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-11T10-06-31.070515.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_01_11T10_06_31.070515
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-11T10-06-31.070515.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-11T10-06-31.070515.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_01_11T10_06_31.070515
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-11T10-06-31.070515.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-11T10-06-31.070515.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_01_11T10_06_31.070515
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-11T10-06-31.070515.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-11T10-06-31.070515.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_01_11T10_06_31.070515
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-11T10-06-31.070515.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-11T10-06-31.070515.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_01_11T10_06_31.070515
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-11T10-06-31.070515.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-11T10-06-31.070515.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_01_11T10_06_31.070515
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-11T10-06-31.070515.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-11T10-06-31.070515.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_01_11T10_06_31.070515
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-11T10-06-31.070515.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-11T10-06-31.070515.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_01_11T10_06_31.070515
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-11T10-06-31.070515.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-11T10-06-31.070515.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_01_11T10_06_31.070515
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-11T10-06-31.070515.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-11T10-06-31.070515.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_01_11T10_06_31.070515
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-11T10-06-31.070515.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-11T10-06-31.070515.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_01_11T10_06_31.070515
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-11T10-06-31.070515.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-11T10-06-31.070515.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_01_11T10_06_31.070515
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-11T10-06-31.070515.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-11T10-06-31.070515.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_01_11T10_06_31.070515
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-11T10-06-31.070515.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-11T10-06-31.070515.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_01_11T10_06_31.070515
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-11T10-06-31.070515.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-11T10-06-31.070515.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_01_11T10_06_31.070515
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-11T10-06-31.070515.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-11T10-06-31.070515.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_01_11T10_06_31.070515
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-11T10-06-31.070515.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-11T10-06-31.070515.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_01_11T10_06_31.070515
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-11T10-06-31.070515.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-11T10-06-31.070515.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_01_11T10_06_31.070515
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-11T10-06-31.070515.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-11T10-06-31.070515.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_01_11T10_06_31.070515
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-11T10-06-31.070515.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-11T10-06-31.070515.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_01_11T10_06_31.070515
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-11T10-06-31.070515.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-11T10-06-31.070515.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_01_11T10_06_31.070515
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-11T10-06-31.070515.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-11T10-06-31.070515.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_01_11T10_06_31.070515
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-11T10-06-31.070515.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-11T10-06-31.070515.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_01_11T10_06_31.070515
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-11T10-06-31.070515.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-11T10-06-31.070515.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_01_11T10_06_31.070515
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-11T10-06-31.070515.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-11T10-06-31.070515.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_01_11T10_06_31.070515
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-11T10-06-31.070515.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-11T10-06-31.070515.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_01_11T10_06_31.070515
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-11T10-06-31.070515.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-11T10-06-31.070515.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_01_11T10_06_31.070515
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-11T10-06-31.070515.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-11T10-06-31.070515.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_01_11T10_06_31.070515
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-11T10-06-31.070515.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-11T10-06-31.070515.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_01_11T10_06_31.070515
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-11T10-06-31.070515.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-11T10-06-31.070515.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_01_11T10_06_31.070515
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-11T10-06-31.070515.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-11T10-06-31.070515.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_01_11T10_06_31.070515
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-11T10-06-31.070515.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-11T10-06-31.070515.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_01_11T10_06_31.070515
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-11T10-06-31.070515.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-11T10-06-31.070515.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_01_11T10_06_31.070515
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-11T10-06-31.070515.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-11T10-06-31.070515.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_01_11T10_06_31.070515
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-11T10-06-31.070515.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-11T10-06-31.070515.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_01_11T10_06_31.070515
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-11T10-06-31.070515.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-11T10-06-31.070515.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_01_11T10_06_31.070515
path:
- '**/details_harness|winogrande|5_2024-01-11T10-06-31.070515.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-01-11T10-06-31.070515.parquet'
- config_name: results
data_files:
- split: 2024_01_11T10_06_31.070515
path:
- results_2024-01-11T10-06-31.070515.parquet
- split: latest
path:
- results_2024-01-11T10-06-31.070515.parquet
---
# Dataset Card for Evaluation run of rishiraj/oswald-2x7b
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [rishiraj/oswald-2x7b](https://huggingface.co/rishiraj/oswald-2x7b) 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_rishiraj__oswald-2x7b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-11T10:06:31.070515](https://huggingface.co/datasets/open-llm-leaderboard/details_rishiraj__oswald-2x7b/blob/main/results_2024-01-11T10-06-31.070515.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": 0.6533236478227353,
"acc_stderr": 0.03188761134034235,
"acc_norm": 0.6556283671019292,
"acc_norm_stderr": 0.032521516691180946,
"mc1": 0.4357405140758874,
"mc1_stderr": 0.017358345398863124,
"mc2": 0.6006314442487943,
"mc2_stderr": 0.015414089468190334
},
"harness|arc:challenge|25": {
"acc": 0.6279863481228669,
"acc_stderr": 0.01412459788184446,
"acc_norm": 0.6646757679180887,
"acc_norm_stderr": 0.013796182947785562
},
"harness|hellaswag|10": {
"acc": 0.6697868950408286,
"acc_stderr": 0.004693285694663836,
"acc_norm": 0.8546106353316073,
"acc_norm_stderr": 0.003517725787017748
},
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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
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[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
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### 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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huggingartists/bruce-springsteen | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/bruce-springsteen"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [About](#about)
## Dataset Description
- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of the generated dataset:** 1.320493 MB
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/6dfe4b89b895b331f09c6b136a0705e5.807x807x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/bruce-springsteen">
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
</a>
<div style="text-align: center; font-size: 16px; font-weight: 800">Bruce Springsteen</div>
<a href="https://genius.com/artists/bruce-springsteen">
<div style="text-align: center; font-size: 14px;">@bruce-springsteen</div>
</a>
</div>
### Dataset Summary
The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
Model is available [here](https://huggingface.co/huggingartists/bruce-springsteen).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
en
## How to use
How to load this dataset directly with the datasets library:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/bruce-springsteen")
```
## Dataset Structure
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
}
```
### Data Fields
The data fields are the same among all splits.
- `text`: a `string` feature.
### Data Splits
| train |validation|test|
|------:|---------:|---:|
|960| -| -|
'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
```python
from datasets import load_dataset, Dataset, DatasetDict
import numpy as np
datasets = load_dataset("huggingartists/bruce-springsteen")
train_percentage = 0.9
validation_percentage = 0.07
test_percentage = 0.03
train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
datasets = DatasetDict(
{
'train': Dataset.from_dict({'text': list(train)}),
'validation': Dataset.from_dict({'text': list(validation)}),
'test': Dataset.from_dict({'text': list(test)})
}
)
```
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{huggingartists,
author={Aleksey Korshuk}
year=2021
}
```
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
gouthamsk/embedded_dataset_mixed_small | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
- name: input
dtype: string
splits:
- name: train
num_bytes: 619551.0
num_examples: 452
download_size: 291969
dataset_size: 619551.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
hltcoe/megawika | ---
license: cc-by-sa-4.0
task_categories:
- summarization
- question-answering
- text-generation
- text2text-generation
language:
- af
- ar
- az
- bn
- cs
- de
- en
- es
- et
- fa
- fi
- fr
- ga
- gl
- gu
- he
- hi
- hr
- id
- it
- ja
- ka
- kk
- km
- ko
- lt
- lv
- mk
- ml
- mn
- mr
- my
- ne
- nl
- pl
- ps
- pt
- ro
- ru
- si
- sl
- sv
- ta
- th
- tr
- uk
- ur
- vi
- xh
- zh
pretty_name: MegaWika
size_categories:
- 10M<n<100M
---
# Dataset Card for MegaWika
## Dataset Description
- **Homepage:** [HuggingFace](https://huggingface.co/datasets/hltcoe/megawika)
- **Repository:** [HuggingFace](https://huggingface.co/datasets/hltcoe/megawika)
- **Paper:** [Coming soon]
- **Leaderboard:** [Coming soon]
- **Point of Contact:** [Samuel Barham](samuel.barham@jhuapl.edu)
### Dataset Summary
MegaWika is a multi- and crosslingual text dataset containing 30 million Wikipedia passages with their scraped and cleaned web citations. The passages span
50 Wikipedias in 50 languages, and the articles in which the passages were originally embedded are included for convenience. Where a Wikipedia passage is in a
non-English language, an automated English translation is provided. Furthermore, nearly 130 million English question/answer pairs were extracted from the
passages, and FrameNet events occurring in the passages are detected using the [LOME](https://aclanthology.org/2021.eacl-demos.19.pdf) FrameNet parser.
<!---
To get a feel for the dataset -- its structure, content, strengths and weaknesses -- you may visit the [dataset viewer](https://huggingface.co/spaces/hltcoe/megawika)
we have set up as a HuggingFace Space. It allows the curious visitor to explore a small set of examples spread across a number of the dataset's constituent languages.
-->
### Dataset Creation
The pipeline through which MegaWika was created is complex, and is described in more detail in the paper (linked above),
but the following diagram illustrates the basic approach.
### Supported Tasks and Leaderboards
MegaWika is meant to support research across a variety of tasks, including report generation, summarization, information retrieval, question answering, etc.
### Languages
MegaWika is divided by Wikipedia language. There are 50 languages, including English, each designated by their 2-character ISO language code:
- `af`: Afrikaans
- `ar`: Arabic
- `az`: Azeri (Azerbaijani)
- `bn`: Bengali
- `cs`: Czech
- `de`: German (Deutsch)
- `en`: English
- `es`: Spanish (Español)
- `et`: Estonian
- `fa`: Farsi (Persian)
- `fi`: Finnish
- `fr`: French
- `ga`: Irish (Gaelic)
- `gl`: Galician
- `gu`: Gujarati
- `he`: Hebrew
- `hi`: Hindi
- `hr`: Hungarian
- `id`: Indonesian
- `it`: Italian
- `ja`: Japanese
- `ka`: Georgian (Kartvelian/Kartlian)
- `kk`: Kazakh
- `km`: Khmer
- `ko`: Korean
- `lt`: Lithuanian
- `lv`: Latvian
- `mk`: Macedonian (Makedonski)
- `ml`: Malay (Malayalam)
- `mn`: Mongolian
- `mr`: Marathi
- `my`: Burmese (Myanmar language)
- `ne`: Nepali
- `nl`: Dutch (Nederlands)
- `pl`: Polish
- `ps`: Pashto
- `pt`: Portuguese
- `ro`: Romanian
- `ru`: Russian
- `si`: Sinhalese (Sri Lankan language)
- `sl`: Slovenian
- `sv`: Swedish (Svenska)
- `ta`: Tamil
- `th`: Thai
- `tr`: Turkish
- `uk`: Ukrainian
- `ur`: Urdu
- `vi`: Vietnamese
- `xh`: Xhosa
- `zh`: Chinese (Zhōng wén)
## Dataset Structure
The dataset is divided by language, and the data for each of the 50 languages is further chunked into discrete JSON lines files.
Each line of these files -- we'll call such a line an **instance** -- contains the data extracted from a single Wikipedia article.
### Data Instances
Each instance contains the text of the seed Wikipedia article, along with a list of **entries**. Each entry consists basically in
an extracted Wikipedia passage, the URL and scraped text of the web source it cites, a list of questions/answer pairs extracted from the passage,
and a framenet parse of the passage. Where the passage is from a non-English Wikipedia, a machine translation into English is also provided.
### Data Fields
The detailed structure of an instance is as follows:
```
{
"article_title": <string : title of original Wikipedia article>
"article_text": <string : text of Wikipedia article>
"entries": [
# Wiki Passage
"id": <string : passage ID>
"passage": {
"text": <string : text of passage in English (possibly via MT)>
"parse": <list of dict : FrameNet parse of English passage text>
"en_tokens": <dict : tokenization of passage in English>
"lang_tokens": <dict : tokenization of original non-English passage>
"en_lang_token_map": <dict : alignment mapping between English and original language token indices>
}
# MT
"original": <string : original language passage>
"original_sents": <list of string : sentencized original language passage>
"translation": <string : machine translation of passage>
"translation_sents": <list of string : sentencized machine translation of passage>
"translation_probs": <list of float : log prob of machine translation by sentence, where available>
"repetitious_translation": <string \in ("true", "false") : automated judgment on whether machine translation is pathologically repetitious>
"source_lang": <string : language ID, 2-character ISO code>
# Source
"source_url": <string : URL of the cited web source>
"source_text": <string : content extracted from the scrape of the source URL>
# Question/Answer Pairs
"qa_pairs": [
...
{
"question": <string : generated question>
"passage_id": <string : passage ID>
"en_answer": <string : English answer>
"lang_answer": <string : aligned original language answer>
"frames": [
...
{
"frame": <string : frame triggered by the question>
"argument": <string : detected frame arguments>
}
...
]
# NB: answer matches can be empty, in the case no matching span exists
"en_matches_in_source": <list of int : start and end index of the English language-answer token(s) in the source document>
"en_match_in_passage": <list of int : start and end index of the English language-answer token(s) in the English language translation of the passage>
"lang_matches_in_source": <list of int : start and end index of the original language-answer token(s) in the source document>
"lang_match_in_passage": <list of int : start and end index of the original language-answer token(s) in the original language passage>
"passage": <list of string : sentencized view of the passage>
"en_answer_tokens": <list of string>
"match_disambiguated_question": <string : disambiguated version of question obtained by matching pronouns with article title (noisy but often helpful)>
}
...
]
]
}
```
English language instances differ not in structure but in content;
1. Fields in the block labeled "MT" above are naturally null (that is, they are set to falsy values in Python -- specifically `None`)
2. Since the Wiki passage only exists in English, and has no corresponding non-English "original language" version, answer spans also necessarily have only an English-language version (and no non-English "original-language" version. Therefore, fields in the `qa_pairs` block beginning with `lang_` are set to null/falsy values in Python (in this case, empty lists).
### Data Splits
MegaWika is currently split only by language, as each task will imply its own approach to filtering, sampling, downselecting, and splitting into train/test splits.
<!---
### 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]
-->
## Licensing and Takedown
MegaWika 1.0 consists in part of documents scraped from across the web (based on citations linked in Wikipedia articles.)
We do not own any of the scraped text nor do we claim copyright: text drawn from Wikipedia citations are meant for research use in algorithmic design and model training.
We release this dataset and all its contents under CC-BY-SA-4.0.
### Notice and Takedown Policy:
*NB*: Should you consider that our data contains material that is owned by you and should therefore not be reproduced here, please:
- Clearly identify yourself, with detailed contact data such as an address, telephone number or email address at which you can be contacted.
- Clearly identify the copyrighted work claimed to be infringed.
- Clearly identify the material that is claimed to be infringing and information reasonably sufficient to allow us to locate the material.
And contact the authors.
*Take down*: We will comply to legitimate requests by removing the affected sources from the next release of the dataset.
## Additional Information
### Dataset Curators
Released and maintained by the Johns Hopkins University Human Language Technology Center of Excellence (JHU/HLTCOE).
You can contact one the MegaWika authors, including [Samuel Barham](mailto:samuel.barham@jhuapl.edu), [Orion Weller](mailto:oweller2@jhu.edu),
and [Ben van Durme](mailto:vandurme@jhu.edu) with questions.
### Licensing Information
Released under the [Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)](https://creativecommons.org/licenses/by-sa/4.0/) license.
### Citation Information
```
@misc{barham2023megawika,
title={MegaWika: Millions of reports and their sources across 50 diverse languages},
author={Samuel Barham and and Weller and Michelle Yuan and Kenton Murray and Mahsa Yarmohammadi and Zhengping Jiang and Siddharth Vashishtha and Alexander Martin and Anqi Liu and Aaron Steven White and Jordan Boyd-Graber and Benjamin Van Durme},
year={2023},
eprint={2307.07049},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
<!--
### Contributions
[More Information Needed]
-->
|
open-llm-leaderboard/details_jeiku__Cookie_7B | ---
pretty_name: Evaluation run of jeiku/Cookie_7B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [jeiku/Cookie_7B](https://huggingface.co/jeiku/Cookie_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_jeiku__Cookie_7B\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-02-17T00:21:45.959538](https://huggingface.co/datasets/open-llm-leaderboard/details_jeiku__Cookie_7B/blob/main/results_2024-02-17T00-21-45.959538.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\": 0.6489384774347392,\n\
\ \"acc_stderr\": 0.032131421920616465,\n \"acc_norm\": 0.6498781521461111,\n\
\ \"acc_norm_stderr\": 0.032783132740631354,\n \"mc1\": 0.5128518971848225,\n\
\ \"mc1_stderr\": 0.01749771794429982,\n \"mc2\": 0.6687534212220169,\n\
\ \"mc2_stderr\": 0.015263939252034519\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6791808873720137,\n \"acc_stderr\": 0.01364094309194653,\n\
\ \"acc_norm\": 0.697098976109215,\n \"acc_norm_stderr\": 0.013428241573185349\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7105158334993029,\n\
\ \"acc_stderr\": 0.004525960965551707,\n \"acc_norm\": 0.8757219677355108,\n\
\ \"acc_norm_stderr\": 0.0032922425436373417\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.38,\n \"acc_stderr\": 0.048783173121456316,\n \
\ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.048783173121456316\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6222222222222222,\n\
\ \"acc_stderr\": 0.04188307537595853,\n \"acc_norm\": 0.6222222222222222,\n\
\ \"acc_norm_stderr\": 0.04188307537595853\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.7171052631578947,\n \"acc_stderr\": 0.03665349695640767,\n\
\ \"acc_norm\": 0.7171052631578947,\n \"acc_norm_stderr\": 0.03665349695640767\n\
\ },\n \"harness|hendrycksTest-business_ethics|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-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\": 0.7430555555555556,\n\
\ \"acc_stderr\": 0.03653946969442099,\n \"acc_norm\": 0.7430555555555556,\n\
\ \"acc_norm_stderr\": 0.03653946969442099\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.47,\n \"acc_stderr\": 0.050161355804659205,\n \
\ \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.050161355804659205\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.54,\n \"acc_stderr\": 0.05009082659620333,\n \"acc_norm\"\
: 0.54,\n \"acc_norm_stderr\": 0.05009082659620333\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621504,\n \
\ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621504\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6705202312138728,\n\
\ \"acc_stderr\": 0.03583901754736412,\n \"acc_norm\": 0.6705202312138728,\n\
\ \"acc_norm_stderr\": 0.03583901754736412\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.38235294117647056,\n \"acc_stderr\": 0.04835503696107224,\n\
\ \"acc_norm\": 0.38235294117647056,\n \"acc_norm_stderr\": 0.04835503696107224\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \"acc_norm\": 0.76,\n\
\ \"acc_norm_stderr\": 0.042923469599092816\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.574468085106383,\n \"acc_stderr\": 0.03232146916224469,\n\
\ \"acc_norm\": 0.574468085106383,\n \"acc_norm_stderr\": 0.03232146916224469\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\": 0.5379310344827586,\n \"acc_stderr\": 0.04154659671707548,\n\
\ \"acc_norm\": 0.5379310344827586,\n \"acc_norm_stderr\": 0.04154659671707548\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.41005291005291006,\n \"acc_stderr\": 0.02533120243894443,\n \"\
acc_norm\": 0.41005291005291006,\n \"acc_norm_stderr\": 0.02533120243894443\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4126984126984127,\n\
\ \"acc_stderr\": 0.04403438954768177,\n \"acc_norm\": 0.4126984126984127,\n\
\ \"acc_norm_stderr\": 0.04403438954768177\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621504,\n \
\ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621504\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7870967741935484,\n\
\ \"acc_stderr\": 0.023287665127268545,\n \"acc_norm\": 0.7870967741935484,\n\
\ \"acc_norm_stderr\": 0.023287665127268545\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.5320197044334976,\n \"acc_stderr\": 0.03510766597959215,\n\
\ \"acc_norm\": 0.5320197044334976,\n \"acc_norm_stderr\": 0.03510766597959215\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.68,\n \"acc_stderr\": 0.04688261722621505,\n \"acc_norm\"\
: 0.68,\n \"acc_norm_stderr\": 0.04688261722621505\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7818181818181819,\n \"acc_stderr\": 0.03225078108306289,\n\
\ \"acc_norm\": 0.7818181818181819,\n \"acc_norm_stderr\": 0.03225078108306289\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.797979797979798,\n \"acc_stderr\": 0.028606204289229865,\n \"\
acc_norm\": 0.797979797979798,\n \"acc_norm_stderr\": 0.028606204289229865\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8756476683937824,\n \"acc_stderr\": 0.023814477086593552,\n\
\ \"acc_norm\": 0.8756476683937824,\n \"acc_norm_stderr\": 0.023814477086593552\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6615384615384615,\n \"acc_stderr\": 0.023991500500313036,\n\
\ \"acc_norm\": 0.6615384615384615,\n \"acc_norm_stderr\": 0.023991500500313036\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.337037037037037,\n \"acc_stderr\": 0.02882088466625326,\n \
\ \"acc_norm\": 0.337037037037037,\n \"acc_norm_stderr\": 0.02882088466625326\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6932773109243697,\n \"acc_stderr\": 0.029953823891887027,\n\
\ \"acc_norm\": 0.6932773109243697,\n \"acc_norm_stderr\": 0.029953823891887027\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.37748344370860926,\n \"acc_stderr\": 0.03958027231121569,\n \"\
acc_norm\": 0.37748344370860926,\n \"acc_norm_stderr\": 0.03958027231121569\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8495412844036697,\n \"acc_stderr\": 0.015328563932669237,\n \"\
acc_norm\": 0.8495412844036697,\n \"acc_norm_stderr\": 0.015328563932669237\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\"\
: 0.8333333333333334,\n \"acc_stderr\": 0.026156867523931045,\n \"\
acc_norm\": 0.8333333333333334,\n \"acc_norm_stderr\": 0.026156867523931045\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8185654008438819,\n \"acc_stderr\": 0.02508596114457966,\n \
\ \"acc_norm\": 0.8185654008438819,\n \"acc_norm_stderr\": 0.02508596114457966\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.695067264573991,\n\
\ \"acc_stderr\": 0.030898610882477515,\n \"acc_norm\": 0.695067264573991,\n\
\ \"acc_norm_stderr\": 0.030898610882477515\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.8015267175572519,\n \"acc_stderr\": 0.034981493854624714,\n\
\ \"acc_norm\": 0.8015267175572519,\n \"acc_norm_stderr\": 0.034981493854624714\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7851239669421488,\n \"acc_stderr\": 0.037494924487096966,\n \"\
acc_norm\": 0.7851239669421488,\n \"acc_norm_stderr\": 0.037494924487096966\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8055555555555556,\n\
\ \"acc_stderr\": 0.038260763248848646,\n \"acc_norm\": 0.8055555555555556,\n\
\ \"acc_norm_stderr\": 0.038260763248848646\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7668711656441718,\n \"acc_stderr\": 0.0332201579577674,\n\
\ \"acc_norm\": 0.7668711656441718,\n \"acc_norm_stderr\": 0.0332201579577674\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4375,\n\
\ \"acc_stderr\": 0.04708567521880525,\n \"acc_norm\": 0.4375,\n \
\ \"acc_norm_stderr\": 0.04708567521880525\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8058252427184466,\n \"acc_stderr\": 0.03916667762822585,\n\
\ \"acc_norm\": 0.8058252427184466,\n \"acc_norm_stderr\": 0.03916667762822585\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8760683760683761,\n\
\ \"acc_stderr\": 0.021586494001281376,\n \"acc_norm\": 0.8760683760683761,\n\
\ \"acc_norm_stderr\": 0.021586494001281376\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\": 0.8275862068965517,\n\
\ \"acc_stderr\": 0.013507943909371803,\n \"acc_norm\": 0.8275862068965517,\n\
\ \"acc_norm_stderr\": 0.013507943909371803\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7283236994219653,\n \"acc_stderr\": 0.023948512905468358,\n\
\ \"acc_norm\": 0.7283236994219653,\n \"acc_norm_stderr\": 0.023948512905468358\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.46368715083798884,\n\
\ \"acc_stderr\": 0.016678341894533166,\n \"acc_norm\": 0.46368715083798884,\n\
\ \"acc_norm_stderr\": 0.016678341894533166\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.738562091503268,\n \"acc_stderr\": 0.025160998214292452,\n\
\ \"acc_norm\": 0.738562091503268,\n \"acc_norm_stderr\": 0.025160998214292452\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7202572347266881,\n\
\ \"acc_stderr\": 0.025494259350694912,\n \"acc_norm\": 0.7202572347266881,\n\
\ \"acc_norm_stderr\": 0.025494259350694912\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7314814814814815,\n \"acc_stderr\": 0.024659685185967284,\n\
\ \"acc_norm\": 0.7314814814814815,\n \"acc_norm_stderr\": 0.024659685185967284\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.4645390070921986,\n \"acc_stderr\": 0.029752389657427047,\n \
\ \"acc_norm\": 0.4645390070921986,\n \"acc_norm_stderr\": 0.029752389657427047\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.45697522816166886,\n\
\ \"acc_stderr\": 0.012722869501611419,\n \"acc_norm\": 0.45697522816166886,\n\
\ \"acc_norm_stderr\": 0.012722869501611419\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6875,\n \"acc_stderr\": 0.02815637344037142,\n \
\ \"acc_norm\": 0.6875,\n \"acc_norm_stderr\": 0.02815637344037142\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6470588235294118,\n \"acc_stderr\": 0.01933314202079716,\n \
\ \"acc_norm\": 0.6470588235294118,\n \"acc_norm_stderr\": 0.01933314202079716\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6727272727272727,\n\
\ \"acc_stderr\": 0.0449429086625209,\n \"acc_norm\": 0.6727272727272727,\n\
\ \"acc_norm_stderr\": 0.0449429086625209\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7224489795918367,\n \"acc_stderr\": 0.028666857790274645,\n\
\ \"acc_norm\": 0.7224489795918367,\n \"acc_norm_stderr\": 0.028666857790274645\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\": 0.82,\n \"acc_stderr\": 0.038612291966536955,\n \
\ \"acc_norm\": 0.82,\n \"acc_norm_stderr\": 0.038612291966536955\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\": 0.847953216374269,\n \"acc_stderr\": 0.027539122889061456,\n\
\ \"acc_norm\": 0.847953216374269,\n \"acc_norm_stderr\": 0.027539122889061456\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.5128518971848225,\n\
\ \"mc1_stderr\": 0.01749771794429982,\n \"mc2\": 0.6687534212220169,\n\
\ \"mc2_stderr\": 0.015263939252034519\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.813733228097869,\n \"acc_stderr\": 0.010941877955676207\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6118271417740713,\n \
\ \"acc_stderr\": 0.013423607564002757\n }\n}\n```"
repo_url: https://huggingface.co/jeiku/Cookie_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_02_17T00_21_45.959538
path:
- '**/details_harness|arc:challenge|25_2024-02-17T00-21-45.959538.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-02-17T00-21-45.959538.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_02_17T00_21_45.959538
path:
- '**/details_harness|gsm8k|5_2024-02-17T00-21-45.959538.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-02-17T00-21-45.959538.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_02_17T00_21_45.959538
path:
- '**/details_harness|hellaswag|10_2024-02-17T00-21-45.959538.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-02-17T00-21-45.959538.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_02_17T00_21_45.959538
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-17T00-21-45.959538.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-17T00-21-45.959538.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-17T00-21-45.959538.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_02_17T00_21_45.959538
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-17T00-21-45.959538.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-17T00-21-45.959538.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_02_17T00_21_45.959538
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-17T00-21-45.959538.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-17T00-21-45.959538.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_02_17T00_21_45.959538
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-17T00-21-45.959538.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-17T00-21-45.959538.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_02_17T00_21_45.959538
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-17T00-21-45.959538.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-17T00-21-45.959538.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_02_17T00_21_45.959538
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-17T00-21-45.959538.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-17T00-21-45.959538.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_02_17T00_21_45.959538
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-17T00-21-45.959538.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-17T00-21-45.959538.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_02_17T00_21_45.959538
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-17T00-21-45.959538.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-17T00-21-45.959538.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_02_17T00_21_45.959538
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-17T00-21-45.959538.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-17T00-21-45.959538.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_02_17T00_21_45.959538
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-17T00-21-45.959538.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-17T00-21-45.959538.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_02_17T00_21_45.959538
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-17T00-21-45.959538.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-17T00-21-45.959538.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_02_17T00_21_45.959538
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-17T00-21-45.959538.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-17T00-21-45.959538.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_02_17T00_21_45.959538
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-17T00-21-45.959538.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-17T00-21-45.959538.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_02_17T00_21_45.959538
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-17T00-21-45.959538.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-17T00-21-45.959538.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_02_17T00_21_45.959538
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-17T00-21-45.959538.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-17T00-21-45.959538.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_02_17T00_21_45.959538
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-17T00-21-45.959538.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-17T00-21-45.959538.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_02_17T00_21_45.959538
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-17T00-21-45.959538.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-17T00-21-45.959538.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_02_17T00_21_45.959538
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-17T00-21-45.959538.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-17T00-21-45.959538.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_02_17T00_21_45.959538
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-17T00-21-45.959538.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-17T00-21-45.959538.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_02_17T00_21_45.959538
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-17T00-21-45.959538.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-17T00-21-45.959538.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_02_17T00_21_45.959538
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-17T00-21-45.959538.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-17T00-21-45.959538.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_02_17T00_21_45.959538
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-17T00-21-45.959538.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-17T00-21-45.959538.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_02_17T00_21_45.959538
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-17T00-21-45.959538.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-17T00-21-45.959538.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_02_17T00_21_45.959538
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-17T00-21-45.959538.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-17T00-21-45.959538.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_02_17T00_21_45.959538
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-17T00-21-45.959538.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-17T00-21-45.959538.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_02_17T00_21_45.959538
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-17T00-21-45.959538.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-17T00-21-45.959538.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_02_17T00_21_45.959538
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-17T00-21-45.959538.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-17T00-21-45.959538.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_02_17T00_21_45.959538
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-17T00-21-45.959538.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-17T00-21-45.959538.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_02_17T00_21_45.959538
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-17T00-21-45.959538.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-17T00-21-45.959538.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_02_17T00_21_45.959538
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-17T00-21-45.959538.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-17T00-21-45.959538.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_02_17T00_21_45.959538
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-17T00-21-45.959538.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-17T00-21-45.959538.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_02_17T00_21_45.959538
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-17T00-21-45.959538.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-17T00-21-45.959538.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_02_17T00_21_45.959538
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-17T00-21-45.959538.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-17T00-21-45.959538.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_02_17T00_21_45.959538
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-17T00-21-45.959538.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-17T00-21-45.959538.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_02_17T00_21_45.959538
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-17T00-21-45.959538.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-17T00-21-45.959538.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_02_17T00_21_45.959538
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-17T00-21-45.959538.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-17T00-21-45.959538.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_02_17T00_21_45.959538
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-17T00-21-45.959538.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-17T00-21-45.959538.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_02_17T00_21_45.959538
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-17T00-21-45.959538.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-17T00-21-45.959538.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_02_17T00_21_45.959538
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-17T00-21-45.959538.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-17T00-21-45.959538.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_02_17T00_21_45.959538
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-17T00-21-45.959538.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-17T00-21-45.959538.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_02_17T00_21_45.959538
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-17T00-21-45.959538.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-17T00-21-45.959538.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_02_17T00_21_45.959538
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-17T00-21-45.959538.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-17T00-21-45.959538.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_02_17T00_21_45.959538
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-17T00-21-45.959538.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-17T00-21-45.959538.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_02_17T00_21_45.959538
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-17T00-21-45.959538.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-17T00-21-45.959538.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_02_17T00_21_45.959538
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-17T00-21-45.959538.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-17T00-21-45.959538.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_02_17T00_21_45.959538
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-17T00-21-45.959538.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-17T00-21-45.959538.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_02_17T00_21_45.959538
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-17T00-21-45.959538.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-17T00-21-45.959538.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_02_17T00_21_45.959538
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-17T00-21-45.959538.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-17T00-21-45.959538.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_02_17T00_21_45.959538
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-17T00-21-45.959538.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-17T00-21-45.959538.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_02_17T00_21_45.959538
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-17T00-21-45.959538.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-17T00-21-45.959538.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_02_17T00_21_45.959538
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-17T00-21-45.959538.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-17T00-21-45.959538.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_02_17T00_21_45.959538
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-17T00-21-45.959538.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-17T00-21-45.959538.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_02_17T00_21_45.959538
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-17T00-21-45.959538.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-17T00-21-45.959538.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_02_17T00_21_45.959538
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-17T00-21-45.959538.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-17T00-21-45.959538.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_02_17T00_21_45.959538
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-17T00-21-45.959538.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-17T00-21-45.959538.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_02_17T00_21_45.959538
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-17T00-21-45.959538.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-17T00-21-45.959538.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_02_17T00_21_45.959538
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-17T00-21-45.959538.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-17T00-21-45.959538.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_02_17T00_21_45.959538
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-17T00-21-45.959538.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-17T00-21-45.959538.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_02_17T00_21_45.959538
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-17T00-21-45.959538.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-17T00-21-45.959538.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_02_17T00_21_45.959538
path:
- '**/details_harness|winogrande|5_2024-02-17T00-21-45.959538.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-02-17T00-21-45.959538.parquet'
- config_name: results
data_files:
- split: 2024_02_17T00_21_45.959538
path:
- results_2024-02-17T00-21-45.959538.parquet
- split: latest
path:
- results_2024-02-17T00-21-45.959538.parquet
---
# Dataset Card for Evaluation run of jeiku/Cookie_7B
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [jeiku/Cookie_7B](https://huggingface.co/jeiku/Cookie_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_jeiku__Cookie_7B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-02-17T00:21:45.959538](https://huggingface.co/datasets/open-llm-leaderboard/details_jeiku__Cookie_7B/blob/main/results_2024-02-17T00-21-45.959538.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": 0.6489384774347392,
"acc_stderr": 0.032131421920616465,
"acc_norm": 0.6498781521461111,
"acc_norm_stderr": 0.032783132740631354,
"mc1": 0.5128518971848225,
"mc1_stderr": 0.01749771794429982,
"mc2": 0.6687534212220169,
"mc2_stderr": 0.015263939252034519
},
"harness|arc:challenge|25": {
"acc": 0.6791808873720137,
"acc_stderr": 0.01364094309194653,
"acc_norm": 0.697098976109215,
"acc_norm_stderr": 0.013428241573185349
},
"harness|hellaswag|10": {
"acc": 0.7105158334993029,
"acc_stderr": 0.004525960965551707,
"acc_norm": 0.8757219677355108,
"acc_norm_stderr": 0.0032922425436373417
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.38,
"acc_stderr": 0.048783173121456316,
"acc_norm": 0.38,
"acc_norm_stderr": 0.048783173121456316
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6222222222222222,
"acc_stderr": 0.04188307537595853,
"acc_norm": 0.6222222222222222,
"acc_norm_stderr": 0.04188307537595853
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.7171052631578947,
"acc_stderr": 0.03665349695640767,
"acc_norm": 0.7171052631578947,
"acc_norm_stderr": 0.03665349695640767
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.61,
"acc_stderr": 0.04902071300001975,
"acc_norm": 0.61,
"acc_norm_stderr": 0.04902071300001975
},
"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": 0.7430555555555556,
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"acc_norm": 0.7430555555555556,
"acc_norm_stderr": 0.03653946969442099
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.47,
"acc_stderr": 0.050161355804659205,
"acc_norm": 0.47,
"acc_norm_stderr": 0.050161355804659205
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.54,
"acc_stderr": 0.05009082659620333,
"acc_norm": 0.54,
"acc_norm_stderr": 0.05009082659620333
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.32,
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"acc_norm": 0.32,
"acc_norm_stderr": 0.04688261722621504
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6705202312138728,
"acc_stderr": 0.03583901754736412,
"acc_norm": 0.6705202312138728,
"acc_norm_stderr": 0.03583901754736412
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.38235294117647056,
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"acc_norm": 0.38235294117647056,
"acc_norm_stderr": 0.04835503696107224
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.76,
"acc_stderr": 0.042923469599092816,
"acc_norm": 0.76,
"acc_norm_stderr": 0.042923469599092816
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.574468085106383,
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"acc_norm": 0.574468085106383,
"acc_norm_stderr": 0.03232146916224469
},
"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": 0.5379310344827586,
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"acc_norm": 0.5379310344827586,
"acc_norm_stderr": 0.04154659671707548
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.41005291005291006,
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"acc_norm": 0.41005291005291006,
"acc_norm_stderr": 0.02533120243894443
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.4126984126984127,
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"acc_norm": 0.4126984126984127,
"acc_norm_stderr": 0.04403438954768177
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.32,
"acc_stderr": 0.04688261722621504,
"acc_norm": 0.32,
"acc_norm_stderr": 0.04688261722621504
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7870967741935484,
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"acc_norm": 0.7870967741935484,
"acc_norm_stderr": 0.023287665127268545
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5320197044334976,
"acc_stderr": 0.03510766597959215,
"acc_norm": 0.5320197044334976,
"acc_norm_stderr": 0.03510766597959215
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.68,
"acc_stderr": 0.04688261722621505,
"acc_norm": 0.68,
"acc_norm_stderr": 0.04688261722621505
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7818181818181819,
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"acc_norm": 0.7818181818181819,
"acc_norm_stderr": 0.03225078108306289
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.797979797979798,
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"acc_norm": 0.797979797979798,
"acc_norm_stderr": 0.028606204289229865
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8756476683937824,
"acc_stderr": 0.023814477086593552,
"acc_norm": 0.8756476683937824,
"acc_norm_stderr": 0.023814477086593552
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6615384615384615,
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"acc_norm": 0.6615384615384615,
"acc_norm_stderr": 0.023991500500313036
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.337037037037037,
"acc_stderr": 0.02882088466625326,
"acc_norm": 0.337037037037037,
"acc_norm_stderr": 0.02882088466625326
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6932773109243697,
"acc_stderr": 0.029953823891887027,
"acc_norm": 0.6932773109243697,
"acc_norm_stderr": 0.029953823891887027
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.37748344370860926,
"acc_stderr": 0.03958027231121569,
"acc_norm": 0.37748344370860926,
"acc_norm_stderr": 0.03958027231121569
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8495412844036697,
"acc_stderr": 0.015328563932669237,
"acc_norm": 0.8495412844036697,
"acc_norm_stderr": 0.015328563932669237
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5231481481481481,
"acc_stderr": 0.03406315360711507,
"acc_norm": 0.5231481481481481,
"acc_norm_stderr": 0.03406315360711507
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8333333333333334,
"acc_stderr": 0.026156867523931045,
"acc_norm": 0.8333333333333334,
"acc_norm_stderr": 0.026156867523931045
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.8185654008438819,
"acc_stderr": 0.02508596114457966,
"acc_norm": 0.8185654008438819,
"acc_norm_stderr": 0.02508596114457966
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.695067264573991,
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"acc_norm_stderr": 0.030898610882477515
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.8015267175572519,
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},
"harness|hendrycksTest-international_law|5": {
"acc": 0.7851239669421488,
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"acc_norm": 0.7851239669421488,
"acc_norm_stderr": 0.037494924487096966
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.8055555555555556,
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"acc_norm": 0.8055555555555556,
"acc_norm_stderr": 0.038260763248848646
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7668711656441718,
"acc_stderr": 0.0332201579577674,
"acc_norm": 0.7668711656441718,
"acc_norm_stderr": 0.0332201579577674
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.4375,
"acc_stderr": 0.04708567521880525,
"acc_norm": 0.4375,
"acc_norm_stderr": 0.04708567521880525
},
"harness|hendrycksTest-management|5": {
"acc": 0.8058252427184466,
"acc_stderr": 0.03916667762822585,
"acc_norm": 0.8058252427184466,
"acc_norm_stderr": 0.03916667762822585
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8760683760683761,
"acc_stderr": 0.021586494001281376,
"acc_norm": 0.8760683760683761,
"acc_norm_stderr": 0.021586494001281376
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.69,
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"acc_norm": 0.69,
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},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8275862068965517,
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"acc_norm": 0.8275862068965517,
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},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.7283236994219653,
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"acc_norm": 0.7283236994219653,
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},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.46368715083798884,
"acc_stderr": 0.016678341894533166,
"acc_norm": 0.46368715083798884,
"acc_norm_stderr": 0.016678341894533166
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.738562091503268,
"acc_stderr": 0.025160998214292452,
"acc_norm": 0.738562091503268,
"acc_norm_stderr": 0.025160998214292452
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.7202572347266881,
"acc_stderr": 0.025494259350694912,
"acc_norm": 0.7202572347266881,
"acc_norm_stderr": 0.025494259350694912
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7314814814814815,
"acc_stderr": 0.024659685185967284,
"acc_norm": 0.7314814814814815,
"acc_norm_stderr": 0.024659685185967284
},
"harness|hendrycksTest-professional_accounting|5": {
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},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.45697522816166886,
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"acc_norm": 0.45697522816166886,
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},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.6875,
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"acc_norm": 0.6875,
"acc_norm_stderr": 0.02815637344037142
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6470588235294118,
"acc_stderr": 0.01933314202079716,
"acc_norm": 0.6470588235294118,
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},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6727272727272727,
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},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7224489795918367,
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"acc_norm": 0.7224489795918367,
"acc_norm_stderr": 0.028666857790274645
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8507462686567164,
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"acc_norm": 0.8507462686567164,
"acc_norm_stderr": 0.02519692987482707
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.82,
"acc_stderr": 0.038612291966536955,
"acc_norm": 0.82,
"acc_norm_stderr": 0.038612291966536955
},
"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": 0.847953216374269,
"acc_stderr": 0.027539122889061456,
"acc_norm": 0.847953216374269,
"acc_norm_stderr": 0.027539122889061456
},
"harness|truthfulqa:mc|0": {
"mc1": 0.5128518971848225,
"mc1_stderr": 0.01749771794429982,
"mc2": 0.6687534212220169,
"mc2_stderr": 0.015263939252034519
},
"harness|winogrande|5": {
"acc": 0.813733228097869,
"acc_stderr": 0.010941877955676207
},
"harness|gsm8k|5": {
"acc": 0.6118271417740713,
"acc_stderr": 0.013423607564002757
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] |
mohdumar/SPHERE_100K | ---
dataset_info:
features:
- name: id
dtype: string
- name: url
dtype: string
- name: title
dtype: string
- name: sha
dtype: string
- name: raw
dtype: string
- name: vector
sequence: float64
splits:
- name: train
num_bytes: 699928624
num_examples: 100000
download_size: 302241341
dataset_size: 699928624
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
pythainlp/blackboard_treebank_prompt | ---
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
splits:
- name: train
num_bytes: 26964824
num_examples: 130454
download_size: 5903386
dataset_size: 26964824
license: cc-by-3.0
task_categories:
- text2text-generation
- text-generation
language:
- th
size_categories:
- 10K<n<100K
---
# Dataset Card for "blackboard_treebank_prompt"
This dataset made from [blackboard treebank](https://bitbucket.org/kaamanita/blackboard-treebank). The dataset want to create Thai sentence by structure.
The original dataset used own tags but we use Universal Dependencies tags, so we convert those tags into Universal Dependencies tags. [See blackboard treebank tags to Universal Dependencies tags](https://github.com/PyThaiNLP/pythainlp/blob/dev/pythainlp/tag/blackboard.py#L56C5-L56C17)
Source code for create dataset: [https://github.com/PyThaiNLP/support-aya-datasets/blob/main/pos/blackboard_treebank_prompt.ipynb](https://github.com/PyThaiNLP/support-aya-datasets/blob/main/pos/blackboard_treebank_prompt.ipynb)
## Template
```
Inputs: จงสร้างประโยคตามโครงสร้าง {pos}:
Targets: Thai sentence
```
pos: [All tag](https://universaldependencies.org/u/pos/)
See more: [blackboard treebank](https://bitbucket.org/kaamanita/blackboard-treebank). |
Temo/alpaca-kartuli-0.1 | ---
license: cc-by-4.0
language:
- ka
size_categories:
- 10K<n<100K
---
# Alpaca-kartuli-0.1
<!-- Provide a quick summary of the dataset. -->
alpaca Dataset-ის ქართულად გადმოთრგმნილი ვერსია.
- **წყარო:** [alpaca-cleaned](https://huggingface.co/datasets/yahma/alpaca-cleaned#dataset-card-for-alpaca-cleaned)
|
AdapterOcean/med_alpaca_standardized_cluster_96_std | ---
dataset_info:
features:
- name: message
dtype: string
- name: message_type
dtype: string
- name: message_id
dtype: int64
- name: conversation_id
dtype: int64
- name: cluster
dtype: float64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 20049249
num_examples: 41543
download_size: 9724843
dataset_size: 20049249
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "med_alpaca_standardized_cluster_96_std"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
hearmeneigh/e621-rising-v3-micro | ---
dataset_info:
features:
- name: source_id
dtype: string
- name: source
dtype: string
- name: image
dtype: image
- name: tags
sequence: string
- name: url
dtype: string
- name: text
dtype: string
- name: selector
dtype: string
splits:
- name: train
num_bytes: 37835842.0
num_examples: 188
download_size: 37637506
dataset_size: 37835842.0
pretty_name: 'E621 Rising V3 Micro Test Image Dataset'
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
tags:
- not-for-all-audiences
---
<div style='background: #ffeef1; border: 1px solid #fd91a4; padding:1em; border-radius:3px; margin-bottom:2em;'>
<h3 style='margin:0'>NSFW</h3>
<p style='margin:0'>This dataset is not suitable for use by minors. The dataset contains X-rated/NFSW content.</p>
</div>
<div style='background: #eefff1; border: 1px solid #a4fd91; padding:1em; border-radius:3px; margin-bottom:2em;'>
<h3 style='margin:0'>For Testing Only</h3>
<p style='margin:0'>Unless you are running tests, you should use the <a href="https://huggingface.co/datasets/hearmeneigh/e621-rising-v3-curated">curated V3 dataset</a>.</p>
</div>
# E621 Rising V3: Micro Test Image Dataset
* **188** images (35MB) downloaded from `e621.net` (90% of samples), `gelbooru.com`, `danbooru.com`, and `rule34.xxx`
|
Kira8558/Myvoice | ---
license: openrail
---
|
mstz/student_performance | ---
language:
- en
tags:
- student performance
- tabular_classification
- binary_classification
pretty_name: Student Performance
size_categories:
- n<1K
task_categories:
- tabular-classification
configs:
- encoding
- math
- writing
- reading
license: cc
---
# Student performance
The [Student performance dataset](https://www.kaggle.com/datasets/ulrikthygepedersen/student_performances) from Kaggle.
| **Configuration** | **Task** | **Description** |
|-------------------|---------------------------|-----------------------------------------------------------------|
| encoding | | Encoding dictionary showing original values of encoded features.|
| math | Binary classification | Has the student passed the math exam? |
| writing | Binary classification | Has the student passed the writing exam? |
| reading | Binary classification | Has the student passed the reading exam? |
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/student_performance", "math")["train"]
```
# Features
|**Feature** |**Type** |
|-----------------------------------|-----------|
|`is_male` |`bool` |
|`ethnicity` |`string` |
|`parental_level_of_education` |`int8` |
|`has_standard_lunch` |`bool` |
|`has_completed_preparation_test` |`bool` |
|`reading_score` |`int64` |
|`writing_score` |`int64` |
|`math_score` |`int64` | |
TigerResearch/en_books | ---
license: apache-2.0
---
|
wjworld/lora_adenoma_dataset | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 86839007.0
num_examples: 761
download_size: 86812371
dataset_size: 86839007.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Nexdata/4720000_Groups_Chinese_Uighur_Parallel_Corpus_Data | ---
license: cc-by-nc-nd-4.0
---
## Description
4,720,000 sets of Chinese and Uighur language parallel translation corpus, data storage format is txt document. Data cleaning, desensitization, and quality inspection have been carried out, which can be used as a basic corpus for text data analysis and in fields such as machine translation.
For more details, please refer to the link: https://www.nexdata.ai/dataset/1185?source=Huggingface
## Storage format
TXT
## Data content
Chinese-Uighur Parallel Corpus Data
## Data size
4.72 million pairs of Chinese-Uighur Parallel Corpus Data. The Chinese sentences contain 22 characters on average
## Language
Chinese, Uighur
## Application scenario
machine translation
## Accuracy rate
90%
# Licensing Information
Commercial License
|
dbschaeffer/schaeffer_thesis_corrected | ---
dataset_info:
features:
- name: audio
dtype: audio
- name: username
dtype: string
- name: Processes
dtype: string
- name: PulseTypology
dtype: string
- name: Complexity
dtype: string
- name: Onset
dtype: string
- name: Offset
dtype: string
- name: Type
dtype: string
- name: MassType
dtype: string
- name: Direction
dtype: string
- name: description
dtype: string
splits:
- name: train
num_bytes: 1918141228
num_examples: 788
download_size: 1608587794
dataset_size: 1918141228
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: cc-by-4.0
---
The SCHAEFFER dataset (Spectro-morphogical Corpus of Human-annotated Audio with Electroacoustic Features for Experimental Research), is a compilation of 788 raw audio data accompanied by human annotations and morphological acoustic features.
The audio files adhere to the concept of Sound Objects introduced by Pierre Scaheffer, a framework for the analysis and creation of sound that focuses on its typological and morphological characteristics.
Inside the dataset, the annotation are provided in the form of free text, while the labels are pre-chosen from a list of classes, making the sound description fit into a suitable framework for digital analysis.
All the sounds within the dataset are under a "CC-By-4.0-attribution" license.
|
pedramaa/arabic-llm-egyption | ---
license: gpl
---
|
kgr123/quality_mcqa_4096 | ---
dataset_info:
features:
- name: context
dtype: string
- name: query
dtype: string
- name: option_0
dtype: string
- name: option_1
dtype: string
- name: option_2
dtype: string
- name: option_3
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 59769299
num_examples: 1732
- name: validation
num_bytes: 12689610
num_examples: 367
- name: test
num_bytes: 12785728
num_examples: 367
download_size: 10323903
dataset_size: 85244637
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
Ironov/mimiV1 | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 4528616.0
num_examples: 58
download_size: 4528547
dataset_size: 4528616.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
TornikeO/pepe-v1 | ---
license: mit
---
|
fathyshalab/massive_social | ---
dataset_info:
features:
- name: id
dtype: string
- name: label
dtype: int64
- name: text
dtype: string
splits:
- name: train
num_bytes: 25788
num_examples: 391
- name: validation
num_bytes: 4344
num_examples: 68
- name: test
num_bytes: 6404
num_examples: 106
download_size: 0
dataset_size: 36536
---
# Dataset Card for "massive_social"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
trunghlt/stem-wiki-articles-based-on-cohere-embedding | ---
license: cc-by-4.0
---
|
JamieWithofs/Deepfake-and-real-images-2 | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': Fake
'1': Real
splits:
- name: train
num_bytes: 193738175.104
num_examples: 3264
- name: test
num_bytes: 60714894.787
num_examples: 1069
- name: validation
num_bytes: 60918989.328
num_examples: 1072
download_size: 316753394
dataset_size: 315372059.219
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
---
|
dannyroxas/audio_dataset | ---
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: sex
dtype: string
- name: emotion
dtype: string
splits:
- name: train
num_bytes: 887623203.111
num_examples: 3397
download_size: 552931093
dataset_size: 887623203.111
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
anan-2024/twitter_dataset_1713112234 | ---
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: 207889
num_examples: 569
download_size: 116026
dataset_size: 207889
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
AdapterOcean/data-standardized_cluster_18 | ---
dataset_info:
features:
- name: text
dtype: string
- name: conversation_id
dtype: int64
- name: embedding
sequence: float64
- name: cluster
dtype: int64
splits:
- name: train
num_bytes: 43695778
num_examples: 4266
download_size: 12523641
dataset_size: 43695778
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "data-standardized_cluster_18"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
hero-nq1310/wiki_30_samples | ---
dataset_info:
features:
- name: Doc
dtype: string
splits:
- name: train
num_bytes: 865470
num_examples: 30
download_size: 437840
dataset_size: 865470
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
ChrisWilson/twitter_dataset_1710963329 | ---
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: 9122
num_examples: 28
download_size: 10643
dataset_size: 9122
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
liuyanchen1015/MULTI_VALUE_stsb_is_am_1s | ---
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: score
dtype: float64
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
- name: dev
num_bytes: 12767
num_examples: 64
- name: test
num_bytes: 3213
num_examples: 28
- name: train
num_bytes: 4686
num_examples: 37
download_size: 22715
dataset_size: 20666
---
# Dataset Card for "MULTI_VALUE_stsb_is_am_1s"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
sablo/oasst2_dpo_pairs_en | ---
dataset_info:
features:
- name: prompt_id
dtype: string
- name: prompt
dtype: string
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
- name: chosen
list:
- name: content
dtype: string
- name: role
dtype: string
- name: rejected
list:
- name: content
dtype: string
- name: role
dtype: string
- name: score_chosen
dtype: float64
- name: score_rejected
dtype: float64
- name: lang
dtype: string
splits:
- name: train
num_bytes: 19979343
num_examples: 4860
- name: test
num_bytes: 1057997
num_examples: 256
download_size: 12010231
dataset_size: 21037340
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
result-kand2-sdxl-wuerst-karlo/94c40829 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 271
num_examples: 10
download_size: 1428
dataset_size: 271
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "94c40829"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
felipesampaio2010/stevienicholsonraifai | ---
license: openrail
---
|
asatsukixxx/newdataset | ---
license: apache-2.0
---
|
adithya7/xlel_wd_dictionary | ---
annotations_creators:
- found
language_creators:
- found
language:
- af
- ar
- be
- bg
- bn
- ca
- cs
- da
- de
- el
- en
- es
- fa
- fi
- fr
- he
- hi
- hu
- id
- it
- ja
- ko
- ml
- mr
- ms
- nl
- 'no'
- pl
- pt
- ro
- ru
- si
- sk
- sl
- sr
- sv
- sw
- ta
- te
- th
- tr
- uk
- vi
- zh
license:
- cc-by-4.0
multilinguality:
- multilingual
pretty_name: XLEL-WD is a multilingual event linking dataset. This supplementary dataset
contains a dictionary of event items from Wikidata. The descriptions for Wikidata
event items are taken from the corresponding multilingual Wikipedia articles.
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories: []
task_ids: []
---
# Dataset Card for XLEL-WD-Dictionary
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** <https://github.com/adithya7/xlel-wd>
- **Repository:** <https://github.com/adithya7/xlel-wd>
- **Paper:** <https://arxiv.org/abs/2204.06535>
- **Leaderboard:** N/A
- **Point of Contact:** Adithya Pratapa
### Dataset Summary
XLEL-WD is a multilingual event linking dataset. This supplementary dataset contains a dictionary of event items from Wikidata. The descriptions for Wikidata event items are taken from the corresponding multilingual Wikipedia articles.
### Supported Tasks and Leaderboards
This dictionary can be used as a part of the event linking task.
### Languages
This dataset contains text from 44 languages. The language names and their ISO 639-1 codes are listed below. For details on the dataset distribution for each language, refer to the original paper.
| Language | Code | Language | Code | Language | Code | Language | Code |
| -------- | ---- | -------- | ---- | -------- | ---- | -------- | ---- |
| Afrikaans | af | Arabic | ar | Belarusian | be | Bulgarian | bg |
| Bengali | bn | Catalan | ca | Czech | cs | Danish | da |
| German | de | Greek | el | English | en | Spanish | es |
| Persian | fa | Finnish | fi | French | fr | Hebrew | he |
| Hindi | hi | Hungarian | hu | Indonesian | id | Italian | it |
| Japanese | ja | Korean | ko | Malayalam | ml | Marathi | mr |
| Malay | ms | Dutch | nl | Norwegian | no | Polish | pl |
| Portuguese | pt | Romanian | ro | Russian | ru | Sinhala | si |
| Slovak | sk | Slovene | sl | Serbian | sr | Swedish | sv |
| Swahili | sw | Tamil | ta | Telugu | te | Thai | th |
| Turkish | tr | Ukrainian | uk | Vietnamese | vi | Chinese | zh |
## Dataset Structure
### Data Instances
Each instance in the `label_dict.jsonl` file follows the below template,
```json
{
"label_id": "830917",
"label_title": "2010 European Aquatics Championships",
"label_desc": "The 2010 European Aquatics Championships were held from 4–15 August 2010 in Budapest and Balatonfüred, Hungary. It was the fourth time that the city of Budapest hosts this event after 1926, 1958 and 2006. Events in swimming, diving, synchronised swimming (synchro) and open water swimming were scheduled.",
"label_lang": "en"
}
```
### Data Fields
| Field | Meaning |
| ----- | ------- |
| `label_id` | Wikidata ID |
| `label_title` | Title for the event, as collected from the corresponding Wikipedia article |
| `label_desc` | Description for the event, as collected from the corresponding Wikipedia article |
| `label_lang` | language used for the title and description |
### Data Splits
This dictionary has a single split, `dictionary`. It contains 10947 event items from Wikidata and a total of 114834 text descriptions collected from multilingual Wikipedia articles.
## Dataset Creation
### Curation Rationale
This datasets helps address the task of event linking. KB linking is extensively studied for entities, but its unclear if the same methodologies can be extended for linking mentions to events from KB. Event items are collected from Wikidata.
### Source Data
#### Initial Data Collection and Normalization
A Wikidata item is considered a potential event if it has spatial and temporal properties. The final event set is collected after post-processing for quality control.
#### Who are the source language producers?
The titles and descriptions for the events are written by Wikipedia contributors.
### Annotations
#### Annotation process
This dataset was automatically compiled from Wikidata. It was post-processed to improve data quality.
#### Who are the annotators?
Wikidata and Wikipedia contributors.
### 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
This dictionary primarily contains eventive nouns from Wikidata. It does not include other event items from Wikidata such as disease outbreak (Q3241045), military offensive (Q2001676), war (Q198), etc.,
## Additional Information
### Dataset Curators
The dataset was curated by Adithya Pratapa, Rishubh Gupta and Teruko Mitamura. The code for collecting the dataset is available at [Github:xlel-wd](https://github.com/adithya7/xlel-wd).
### Licensing Information
XLEL-WD dataset is released under [CC-BY-4.0 license](https://creativecommons.org/licenses/by/4.0/).
### Citation Information
```bib
@article{pratapa-etal-2022-multilingual,
title = {Multilingual Event Linking to Wikidata},
author = {Pratapa, Adithya and Gupta, Rishubh and Mitamura, Teruko},
publisher = {arXiv},
year = {2022},
url = {https://arxiv.org/abs/2204.06535},
}
```
### Contributions
Thanks to [@adithya7](https://github.com/adithya7) for adding this dataset.
|
thebfbdfiobsesser/Idkeaither | ---
license: afl-3.0
---
|
CyberHarem/saijou_juri_theidolmstershinycolors | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of saijou_juri/西城樹里 (THE iDOLM@STER: SHINY COLORS)
This is the dataset of saijou_juri/西城樹里 (THE iDOLM@STER: SHINY COLORS), containing 500 images and their tags.
The core tags of this character are `blonde_hair, short_hair, purple_eyes, bangs, breasts, hair_between_eyes`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-------------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 500 | 772.49 MiB | [Download](https://huggingface.co/datasets/CyberHarem/saijou_juri_theidolmstershinycolors/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 395.56 MiB | [Download](https://huggingface.co/datasets/CyberHarem/saijou_juri_theidolmstershinycolors/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1205 | 844.99 MiB | [Download](https://huggingface.co/datasets/CyberHarem/saijou_juri_theidolmstershinycolors/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 655.66 MiB | [Download](https://huggingface.co/datasets/CyberHarem/saijou_juri_theidolmstershinycolors/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1205 | 1.25 GiB | [Download](https://huggingface.co/datasets/CyberHarem/saijou_juri_theidolmstershinycolors/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/saijou_juri_theidolmstershinycolors',
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 | 23 |  |  |  |  |  | 1girl, solo, looking_at_viewer, bikini_under_clothes, blush, collarbone, white_shirt, navel, necklace, short_sleeves, black_bikini, cleavage, medium_breasts, short_shorts, tied_shirt, midriff, button_badge, bracelet, visor_cap, blue_sky, day, front-tie_top, black_shorts, denim_shorts, outdoors |
| 1 | 8 |  |  |  |  |  | 1girl, looking_at_viewer, solo, white_shirt, simple_background, upper_body, white_background, blush, collarbone, jacket, collared_shirt, school_uniform, long_sleeves, open_mouth, smile |
| 2 | 19 |  |  |  |  |  | plaid_skirt, 1girl, pleated_skirt, solo, white_shirt, looking_at_viewer, school_uniform, brown_jacket, simple_background, white_background, blush, bracelet, hands_in_pockets, miniskirt, blazer, collared_shirt, open_jacket, open_mouth |
| 3 | 7 |  |  |  |  |  | 1girl, black_jacket, earrings, solo, looking_at_viewer, red_dress, blush, open_jacket, outdoors, floral_print, long_sleeves, blurry, cloud, print_dress, sky, smile |
| 4 | 8 |  |  |  |  |  | 1boy, 1girl, blush, hetero, navel, nipples, solo_focus, collarbone, sweat, completely_nude, looking_at_viewer, small_breasts, closed_mouth, female_pubic_hair, mosaic_censoring, pussy, spread_legs, vaginal, penis, sex, medium_breasts |
| 5 | 11 |  |  |  |  |  | 1girl, black_shirt, necklace, solo, white_shorts, looking_at_viewer, short_sleeves, smile, blush, collarbone, clothes_writing, earrings, jacket_around_waist, ear_piercing, day, one_eye_closed, outdoors, plaid, short_shorts, sky, wristwatch |
| 6 | 7 |  |  |  |  |  | 1girl, formal, solo, suit, black_necktie, simple_background, vest, white_shirt, black_gloves, collared_shirt, looking_at_viewer, white_background, hand_in_pocket, long_sleeves, black_jacket, black_pants, jacket_removed |
| 7 | 6 |  |  |  |  |  | 1girl, looking_at_viewer, sleeveless_dress, solo, white_dress, hairclip, blush, breast_pocket, outdoors, bracelet, collared_dress, grin, leaf, sunlight |
| 8 | 11 |  |  |  |  |  | detached_collar, fake_animal_ears, playboy_bunny, rabbit_ears, 1girl, blush, looking_at_viewer, solo, strapless_leotard, bowtie, wrist_cuffs, cowboy_shot, small_breasts, bare_shoulders, black_pantyhose, simple_background, black_leotard, white_background, cleavage, rabbit_tail, covered_navel, fake_tail, fishnet_pantyhose |
| 9 | 5 |  |  |  |  |  | 1girl, blush, detached_collar, looking_at_viewer, maid_headdress, puffy_short_sleeves, solo, white_apron, enmaided, hair_bow, simple_background, cleavage, collarbone, heart, maid_apron, plaid, wrist_cuffs, closed_mouth, fang, frilled_apron, frilled_dress, open_mouth, pink_background, red_bowtie, small_breasts, sweatdrop, v-shaped_eyebrows, waist_apron, white_background, white_thighhighs |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | looking_at_viewer | bikini_under_clothes | blush | collarbone | white_shirt | navel | necklace | short_sleeves | black_bikini | cleavage | medium_breasts | short_shorts | tied_shirt | midriff | button_badge | bracelet | visor_cap | blue_sky | day | front-tie_top | black_shorts | denim_shorts | outdoors | simple_background | upper_body | white_background | jacket | collared_shirt | school_uniform | long_sleeves | open_mouth | smile | plaid_skirt | pleated_skirt | brown_jacket | hands_in_pockets | miniskirt | blazer | open_jacket | black_jacket | earrings | red_dress | floral_print | blurry | cloud | print_dress | sky | 1boy | hetero | nipples | solo_focus | sweat | completely_nude | small_breasts | closed_mouth | female_pubic_hair | mosaic_censoring | pussy | spread_legs | vaginal | penis | sex | black_shirt | white_shorts | clothes_writing | jacket_around_waist | ear_piercing | one_eye_closed | plaid | wristwatch | formal | suit | black_necktie | vest | black_gloves | hand_in_pocket | black_pants | jacket_removed | sleeveless_dress | white_dress | hairclip | breast_pocket | collared_dress | grin | leaf | sunlight | detached_collar | fake_animal_ears | playboy_bunny | rabbit_ears | strapless_leotard | bowtie | wrist_cuffs | cowboy_shot | bare_shoulders | black_pantyhose | black_leotard | rabbit_tail | covered_navel | fake_tail | fishnet_pantyhose | maid_headdress | puffy_short_sleeves | white_apron | enmaided | hair_bow | heart | maid_apron | fang | frilled_apron | frilled_dress | pink_background | red_bowtie | sweatdrop | v-shaped_eyebrows | waist_apron | white_thighhighs |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------------------|:-----------------------|:--------|:-------------|:--------------|:--------|:-----------|:----------------|:---------------|:-----------|:-----------------|:---------------|:-------------|:----------|:---------------|:-----------|:------------|:-----------|:------|:----------------|:---------------|:---------------|:-----------|:--------------------|:-------------|:-------------------|:---------|:-----------------|:-----------------|:---------------|:-------------|:--------|:--------------|:----------------|:---------------|:-------------------|:------------|:---------|:--------------|:---------------|:-----------|:------------|:---------------|:---------|:--------|:--------------|:------|:-------|:---------|:----------|:-------------|:--------|:------------------|:----------------|:---------------|:--------------------|:-------------------|:--------|:--------------|:----------|:--------|:------|:--------------|:---------------|:------------------|:----------------------|:---------------|:-----------------|:--------|:-------------|:---------|:-------|:----------------|:-------|:---------------|:-----------------|:--------------|:-----------------|:-------------------|:--------------|:-----------|:----------------|:-----------------|:-------|:-------|:-----------|:------------------|:-------------------|:----------------|:--------------|:--------------------|:---------|:--------------|:--------------|:-----------------|:------------------|:----------------|:--------------|:----------------|:------------|:--------------------|:-----------------|:----------------------|:--------------|:-----------|:-----------|:--------|:-------------|:-------|:----------------|:----------------|:------------------|:-------------|:------------|:--------------------|:--------------|:-------------------|
| 0 | 23 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 8 |  |  |  |  |  | X | X | X | | X | X | X | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 19 |  |  |  |  |  | X | X | X | | X | | X | | | | | | | | | | | X | | | | | | | | X | | X | | X | X | | X | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 7 |  |  |  |  |  | X | X | X | | X | | | | | | | | | | | | | | | | | | | | X | | | | | | | X | | X | | | | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 8 |  |  |  |  |  | X | | X | | X | X | | X | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 11 |  |  |  |  |  | X | X | X | | X | X | | | X | X | | | | X | | | | | | | X | | | | X | | | | | | | | | X | | | | | | | | | X | | | | | | X | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 7 |  |  |  |  |  | X | X | X | | | | X | | | | | | | | | | | | | | | | | | | X | | X | | X | | X | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 6 |  |  |  |  |  | X | X | X | | X | | | | | | | | | | | | | X | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 8 | 11 |  |  |  |  |  | X | X | X | | X | | | | | | | X | | | | | | | | | | | | | | X | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | |
| 9 | 5 |  |  |  |  |  | X | X | X | | X | X | | | | | | X | | | | | | | | | | | | | | X | | X | | | | | X | | | | | | | | | | | | | | | | | | | | | | | X | X | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | X | | | | | | X | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
tyouisen/aclue | ---
license: cc-by-nc-4.0
task_categories:
- multiple-choice
- question-answering
language:
- zh
tags:
- llm
- Ancient Chinese
- Evaluation
- chinese
pretty_name: ACLUE
size_categories:
- 1M<n<10M
---
# Dataset Card for ACLUE
- **Homepage:** [https://github.com/isen-zhang/ACLUE](https://github.com/isen-zhang/ACLUE)
- **Repository:** [https://huggingface.co/datasets/tyouisen/aclue](https://huggingface.co/datasets/tyouisen/aclue)
- **Paper:** [https://arxiv.org/abs/2310.0955](https://arxiv.org/abs/2310.0955)
- **Leaderboard:** [https://github.com/isen-zhang/ACLUE](https://github.com/isen-zhang/ACLUE)
### 简介 （Introduction）
Ancient Chinese Language Understanding Evaluation (ACLUE) 是一个面向古代汉语的评估基准，旨在帮助评估大型语言模型在古代汉语上的表现。
The Ancient Chinese Language Understanding Evaluation (ACLUE) is an evaluation benchmark focused on ancient Chinese language comprehension. It aims to assess the performance of large-scale language models (LLMs) on understanding ancient Chinese.
### 数据 （Data）
该基准测试包含15个任务，涵盖了各个领域，包括词汇、句法、语义、推理和知识。我们为这15个任务提供了开发集和测试集数据，开发集中有5个问题，而测试集中则有100多个问题。我们鼓励研究人员使用ACLUE来测试和提升其模型在古代汉语语言理解方面的能力。ACLUE的任务取自人工挑选的公开资源和自动生成的古代汉语语料库。这些问题涵盖了从夏朝（公元前2070年）到明朝（公元1368年）的广泛时间范围。ACLUE对所有任务都采用了多项选择题的形式。
The benchmark comprises 15 tasks spanning various domains, including lexical, syntactic, semantic, inference, and knowledge. We provide development and test dataset for each of 15 tasks, with 5 questions in development set and 100+ quesitons in test set. We encourage researchers to use ACLUE to test and enhance their models' abilities in ancient Chinese language understanding. ACLUE's tasks are derived from a combination of manually curated questions from publicly available resources, and automatic generated questions from classical Chinese language corpora. The range of questions span from the Xia dynasty (2070 BCE) to the Ming dynasty (1368 CE). ACLUE employs a multiple-choice question format for all tasks.
### 数据实例（ Data Instances）
数据集中的每个问题都是一个包含4个选项的多项选择题，其中只有一个选项是正确答案。以下是两个示例：
Each question in the dataset is a multiple-choice questions with 4 choices and only one choice as the correct answer. Here are two examples:
```
以下是关于{古诗词曲鉴赏}的单项选择题，请直接给出正确答案的选项。
题目：《木兰诗--北朝民歌》唧唧复唧唧,木兰当户织。不闻机杼声,唯闻女叹息。问女何所思,问女何所忆。女亦无所思,女亦无所忆。昨夜见军帖,可汗大点兵,军书十二卷,卷卷有爷名。阿爷无大儿,木兰无长兄,愿为市鞍马,从此替爷征。东市买骏马,西市买鞍鞯,南市买辔头,北市买长鞭。旦辞爷娘去,暮宿黄河边,不闻爷娘唤女声,但闻黄河流水鸣溅溅。旦辞黄河去,暮至黑山头,不闻爷娘唤女声,但闻燕山胡骑鸣啾啾。万里赴戎机,关山度若飞。朔气传金柝,寒光照铁衣。将军百战死,壮士十年归。归来见天子,天子坐明堂。策勋十二转,赏赐百千强。可汗问所欲,木兰不用尚书郎,愿驰千里足,送儿还故乡。爷娘闻女来,出郭相扶将;阿姊闻妹来,当户理红妆;小弟闻姊来,磨刀霍霍向猪羊。开我东阁门,坐我西阁床。脱我战时袍,著我旧时裳。当窗理云鬓,对镜帖花黄。出门看火伴,火伴皆惊忙:同行十二年,不知木兰是女郎。雄兔脚扑朔,雌兔眼迷离;双兔傍地走,安能辨我是雄雌?下列对这首诗的理解和分析,不正确的一项是 ()
A. 《木兰诗》是南北朝时期的一首长篇叙事民歌,风格刚健质朴。全诗以“木兰是女郎”来构思木兰的传奇故事,富有浪漫色彩。
B. “愿为市鞍马”的“市”是“市场”的意思,“万里赴戎机”的“戎机”是“战事”的意思。
C. 木兰“不用尚书郎”而愿“还故乡”固然有对家乡的眷恋,但也有自己女儿身秘密的因素。
D. “朔气传金柝,寒光照铁衣”运用对偶手法,描写了木兰在边塞艰苦的军旅生活。
答案是：B
```
```
题目：《虞美人》李煜。春花秋月何时了？往事知多少。小楼昨夜又东风，故国不堪回首月明中。雕栏玉砌应犹在，只是朱颜改。问君能有几多愁？恰似一江春水向东流。对《虞美人》的赏析,不恰当的一项是（）
A. 词作从眼前景物入手,生发联想和想像,追怀昔日帝王生活,描摹了一幅幅鲜活的画面,隐晦地表达出叛逆之情,惹恼了宋太宗,铸成了词人悲惨结局。
B. 词作以实虚相间的手法来绘景、抒情、达意,忽而写眼前,忽而写想像。
C. 《虞美人》乃李煜绝笔词
D. 《虞美人》以其形式别致给人美感愉悦。
答案是：
```
以下列出了任务的类别、实例数量、问题平均长度以及任务的来源：
The category, number of instances, average length of the question, and origin of the tasks are provided below:
| Task | Total Q. | Avg. len |Task (zh) | Category | Origin |
|-------------------------------|------|------|-----------------------------------|----------|-----------|
| Named entity recognition | 500 | 138 | 古汉语命名体识别 | lexical | generated |
| Polysemy resolution | 500 | 116 | 古文单字多义 | lexical | generated |
| Homographic character resolution | 500 | 137 | 通假字 | lexical | generated |
| Sentence segmentation | 500 | 210 | 古文断句 | syntactic| generated |
| Couplet prediction | 500 | 62 | 对联预测 | semantic | generated |
| Poetry context prediction | 500 | 77 | 古诗词上下句预测 | semantic | generated |
| Poetry sentiment analysis | 500 | 60 | 诗词情感分类 | inference| generated |
| Poem quality estimation | 406 | 118 | 古诗词质量评估 | inference| generated |
| Ancient Chinese medical | 211 | 38 | 医古文 | knowledge| collected |
| Ancient Chinese literature | 160 | 44 | 古代文学知识 | knowledge| collected |
| Traditional Chinese culture | 136 | 59 | 国学常识 | knowledge| collected |
| Poetry appreciation | 103 | 258 | 古诗词曲鉴赏 | inference| collected |
| Basic ancient Chinese | 249 | 52 | 基础古汉语知识 | knowledge| collected |
| Reading comprehension | 101 | 982 | 古文阅读理解 | inference| collected |
| Ancient Chinese phonetics | 101 | 50 | 古音学 | knowledge| collected |
#### 加载数据 （Load data）
```python
task_list = ['polysemy_resolution',
'poetry_sentiment_analysis',
'named_entity_recognition',
'basic_ancient_chinese',
'poetry_context_prediction',
'sentence_segmentation',
'couplet_prediction',
'poetry_appreciate',
'ancient_chinese_culture',
'ancient_phonetics',
'homographic_character_resolution',
'ancient_literature',
'ancient_medical',
'poetry_quality_assessment',
'reading_comprehension']
from datasets import load_dataset
dataset = {k: load_dataset(r"tyouisen/aclue", k) for k in task_list}
# Print an example:
print(dataset['polysemy_resolution']['test'][0])
# Or download specific dataset:
dataset = load_dataset("tyouisen/aclue", "couplet_prediction", split="test") # or split = "dev"
```
### 引用 （Citation）
```
@inproceedings{zhang-li-2023-large,
title = "Can Large Langauge Model Comprehend {A}ncient {C}hinese? A Preliminary Test on {ACLUE}",
author = "Zhang, Yixuan and Li, Haonan",
booktitle = "Proceedings of the Ancient Language Processing Workshop",
month = sep,
year = "2023",
address = "Varna, Bulgaria",
publisher = "INCOMA Ltd., Shoumen, Bulgaria",
url = "https://aclanthology.org/2023.alp-1.9",
pages = "80--87"
}
```
### 许可证 （License）
ACLUE数据集采用：(The ACLUE dataset is licensed under a：)
[Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](http://creativecommons.org/licenses/by-nc-sa/4.0/).
|
JohnnyYu22/CPSC2018_original_length | ---
license: other
license_name: other
license_link: LICENSE
---
|
qazisaad/llama_2-product-titles-esci-test-temp | ---
dataset_info:
features:
- name: index
dtype: int64
- name: query
dtype: string
- name: average_score
dtype: float64
- name: total_score
dtype: float64
- name: text
dtype: string
- name: label
dtype: string
- name: preds
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 9174461
num_examples: 4140
download_size: 1472396
dataset_size: 9174461
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "llama_2-product-titles-esci-test-temp"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
yzhuang/autotree_automl_electricity_gosdt_l512_d3_sd1 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: input_x
sequence:
sequence: float64
- name: input_y
sequence:
sequence: float32
- name: rtg
sequence: float64
- name: status
sequence:
sequence: float32
- name: split_threshold
sequence:
sequence: float64
- name: split_dimension
sequence: int64
splits:
- name: train
num_bytes: 5538400000
num_examples: 100000
- name: validation
num_bytes: 553840000
num_examples: 10000
download_size: 1560840336
dataset_size: 6092240000
---
# Dataset Card for "autotree_automl_electricity_gosdt_l512_d3_sd1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
xezpeleta/oasst2_eu | ---
dataset_info:
features:
- name: message_id
dtype: string
- name: parent_id
dtype: string
- name: user_id
dtype: string
- name: created_date
dtype: string
- name: text
dtype: string
- name: role
dtype: string
- name: lang
dtype: string
- name: review_count
dtype: int64
- name: review_result
dtype: bool
- name: deleted
dtype: bool
- name: rank
dtype: float64
- name: synthetic
dtype: bool
- name: model_name
dtype: 'null'
- name: detoxify
struct:
- name: identity_attack
dtype: float64
- name: insult
dtype: float64
- name: obscene
dtype: float64
- name: severe_toxicity
dtype: float64
- name: sexual_explicit
dtype: float64
- name: threat
dtype: float64
- name: toxicity
dtype: float64
- name: message_tree_id
dtype: string
- name: tree_state
dtype: string
- name: emojis
struct:
- name: count
sequence: int64
- name: name
sequence: string
- name: labels
struct:
- name: count
sequence: int64
- name: name
sequence: string
- name: value
sequence: float64
splits:
- name: train
num_bytes: 127018436
num_examples: 125009
- name: validation
num_bytes: 4883575
num_examples: 4759
download_size: 43753635
dataset_size: 131902011
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
|
clips/mfaq | ---
annotations_creators:
- no-annotation
language_creators:
- other
language:
- cs
- da
- de
- en
- es
- fi
- fr
- he
- hr
- hu
- id
- it
- nl
- 'no'
- pl
- pt
- ro
- ru
- sv
- tr
- vi
license:
- cc0-1.0
multilinguality:
- multilingual
pretty_name: MFAQ - a Multilingual FAQ Dataset
size_categories:
- unknown
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- multiple-choice-qa
---
# MFAQ
🚨 See [MQA](https://huggingface.co/datasets/clips/mqa) or [MFAQ Light](maximedb/mfaq_light) for an updated version of the dataset.
MFAQ is a multilingual corpus of *Frequently Asked Questions* parsed from the [Common Crawl](https://commoncrawl.org/).
```
from datasets import load_dataset
load_dataset("clips/mfaq", "en")
{
"qa_pairs": [
{
"question": "Do I need a rental Car in Cork?",
"answer": "If you plan on travelling outside of Cork City, for instance to Kinsale [...]"
},
...
]
}
```
## Languages
We collected around 6M pairs of questions and answers in 21 different languages. To download a language specific subset you need to specify the language key as configuration. See below for an example.
```
load_dataset("clips/mfaq", "en") # replace "en" by any language listed below
```
| Language | Key | Pairs | Pages |
|------------|-----|-----------|-----------|
| All | all | 6,346,693 | 1,035,649 |
| English | en | 3,719,484 | 608,796 |
| German | de | 829,098 | 111,618 |
| Spanish | es | 482,818 | 75,489 |
| French | fr | 351,458 | 56,317 |
| Italian | it | 155,296 | 24,562 |
| Dutch | nl | 150,819 | 32,574 |
| Portuguese | pt | 138,778 | 26,169 |
| Turkish | tr | 102,373 | 19,002 |
| Russian | ru | 91,771 | 22,643 |
| Polish | pl | 65,182 | 10,695 |
| Indonesian | id | 45,839 | 7,910 |
| Norwegian | no | 37,711 | 5,143 |
| Swedish | sv | 37,003 | 5,270 |
| Danish | da | 32,655 | 5,279 |
| Vietnamese | vi | 27,157 | 5,261 |
| Finnish | fi | 20,485 | 2,795 |
| Romanian | ro | 17,066 | 3,554 |
| Czech | cs | 16,675 | 2,568 |
| Hebrew | he | 11,212 | 1,921 |
| Hungarian | hu | 8,598 | 1,264 |
| Croatian | hr | 5,215 | 819 |
## Data Fields
#### Nested (per page - default)
The data is organized by page. Each page contains a list of questions and answers.
- **id**
- **language**
- **num_pairs**: the number of FAQs on the page
- **domain**: source web domain of the FAQs
- **qa_pairs**: a list of questions and answers
- **question**
- **answer**
- **language**
#### Flattened
The data is organized by pair (i.e. pages are flattened). You can access the flat version of any language by appending `_flat` to the configuration (e.g. `en_flat`). The data will be returned pair-by-pair instead of page-by-page.
- **domain_id**
- **pair_id**
- **language**
- **domain**: source web domain of the FAQs
- **question**
- **answer**
## Source Data
This section was adapted from the source data description of [OSCAR](https://huggingface.co/datasets/oscar#source-data)
Common Crawl is a non-profit foundation which produces and maintains an open repository of web crawled data that is both accessible and analysable. Common Crawl's complete web archive consists of petabytes of data collected over 8 years of web crawling. The repository contains raw web page HTML data (WARC files), metdata extracts (WAT files) and plain text extracts (WET files). The organisation's crawlers has always respected nofollow and robots.txt policies.
To construct MFAQ, the WARC files of Common Crawl were used. We looked for `FAQPage` markup in the HTML and subsequently parsed the `FAQItem` from the page.
## People
This model was developed by [Maxime De Bruyn](https://www.linkedin.com/in/maximedebruyn/), Ehsan Lotfi, Jeska Buhmann and Walter Daelemans.
## Licensing Information
```
These data are released under this licensing scheme.
We do not own any of the text from which these data has been extracted.
We license the actual packaging of these data under the Creative Commons CC0 license ("no rights reserved") http://creativecommons.org/publicdomain/zero/1.0/
Should you consider that our data contains material that is owned by you and should therefore not be reproduced here, please:
* Clearly identify yourself, with detailed contact data such as an address, telephone number or email address at which you can be contacted.
* Clearly identify the copyrighted work claimed to be infringed.
* Clearly identify the material that is claimed to be infringing and information reasonably sufficient to allow us to locate the material.
We will comply to legitimate requests by removing the affected sources from the next release of the corpus.
```
## Citation information
```
@misc{debruyn2021mfaq,
title={MFAQ: a Multilingual FAQ Dataset},
author={Maxime {De Bruyn} and Ehsan Lotfi and Jeska Buhmann and Walter Daelemans},
year={2021},
eprint={2109.12870},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
MohammedNasri/NewArabicDataset | ---
dataset_info:
features:
- name: input_features
sequence:
sequence: float32
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 75789246624
num_examples: 78899
- name: test
num_bytes: 10027780960
num_examples: 10440
download_size: 13566982393
dataset_size: 85817027584
---
# Dataset Card for "NewArabicDataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_DreadPoor__ToppyEvil-7B-slerp | ---
pretty_name: Evaluation run of DreadPoor/ToppyEvil-7B-slerp
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [DreadPoor/ToppyEvil-7B-slerp](https://huggingface.co/DreadPoor/ToppyEvil-7B-slerp)\
\ 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_DreadPoor__ToppyEvil-7B-slerp\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-02-01T18:32:26.393415](https://huggingface.co/datasets/open-llm-leaderboard/details_DreadPoor__ToppyEvil-7B-slerp/blob/main/results_2024-02-01T18-32-26.393415.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\": 0.6371495869198338,\n\
\ \"acc_stderr\": 0.03235698893021149,\n \"acc_norm\": 0.6394933656367955,\n\
\ \"acc_norm_stderr\": 0.03300210838103083,\n \"mc1\": 0.3157894736842105,\n\
\ \"mc1_stderr\": 0.016272287957916916,\n \"mc2\": 0.4605602132329784,\n\
\ \"mc2_stderr\": 0.014970488994464466\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6100682593856656,\n \"acc_stderr\": 0.014252959848892893,\n\
\ \"acc_norm\": 0.636518771331058,\n \"acc_norm_stderr\": 0.014056207319068283\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6714797849034057,\n\
\ \"acc_stderr\": 0.00468715199479107,\n \"acc_norm\": 0.8428599880501892,\n\
\ \"acc_norm_stderr\": 0.00363188949612254\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001975,\n \
\ \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001975\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.562962962962963,\n\
\ \"acc_stderr\": 0.042849586397534015,\n \"acc_norm\": 0.562962962962963,\n\
\ \"acc_norm_stderr\": 0.042849586397534015\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6842105263157895,\n \"acc_stderr\": 0.03782728980865469,\n\
\ \"acc_norm\": 0.6842105263157895,\n \"acc_norm_stderr\": 0.03782728980865469\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.58,\n\
\ \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.58,\n \
\ \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6792452830188679,\n \"acc_stderr\": 0.028727502957880267,\n\
\ \"acc_norm\": 0.6792452830188679,\n \"acc_norm_stderr\": 0.028727502957880267\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7361111111111112,\n\
\ \"acc_stderr\": 0.03685651095897532,\n \"acc_norm\": 0.7361111111111112,\n\
\ \"acc_norm_stderr\": 0.03685651095897532\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620332,\n \
\ \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620332\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\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \
\ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6705202312138728,\n\
\ \"acc_stderr\": 0.03583901754736412,\n \"acc_norm\": 0.6705202312138728,\n\
\ \"acc_norm_stderr\": 0.03583901754736412\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.37254901960784315,\n \"acc_stderr\": 0.04810840148082635,\n\
\ \"acc_norm\": 0.37254901960784315,\n \"acc_norm_stderr\": 0.04810840148082635\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.77,\n \"acc_stderr\": 0.04229525846816508,\n \"acc_norm\": 0.77,\n\
\ \"acc_norm_stderr\": 0.04229525846816508\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5361702127659574,\n \"acc_stderr\": 0.03260038511835771,\n\
\ \"acc_norm\": 0.5361702127659574,\n \"acc_norm_stderr\": 0.03260038511835771\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5087719298245614,\n\
\ \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.5087719298245614,\n\
\ \"acc_norm_stderr\": 0.04702880432049615\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\"\
: 0.43915343915343913,\n \"acc_stderr\": 0.025559920550531003,\n \"\
acc_norm\": 0.43915343915343913,\n \"acc_norm_stderr\": 0.025559920550531003\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4365079365079365,\n\
\ \"acc_stderr\": 0.04435932892851466,\n \"acc_norm\": 0.4365079365079365,\n\
\ \"acc_norm_stderr\": 0.04435932892851466\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \
\ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\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\": 0.4876847290640394,\n \"acc_stderr\": 0.035169204442208966,\n\
\ \"acc_norm\": 0.4876847290640394,\n \"acc_norm_stderr\": 0.035169204442208966\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.68,\n \"acc_stderr\": 0.04688261722621505,\n \"acc_norm\"\
: 0.68,\n \"acc_norm_stderr\": 0.04688261722621505\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7515151515151515,\n \"acc_stderr\": 0.033744026441394036,\n\
\ \"acc_norm\": 0.7515151515151515,\n \"acc_norm_stderr\": 0.033744026441394036\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7929292929292929,\n \"acc_stderr\": 0.028869778460267045,\n \"\
acc_norm\": 0.7929292929292929,\n \"acc_norm_stderr\": 0.028869778460267045\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8652849740932642,\n \"acc_stderr\": 0.024639789097709443,\n\
\ \"acc_norm\": 0.8652849740932642,\n \"acc_norm_stderr\": 0.024639789097709443\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6410256410256411,\n \"acc_stderr\": 0.02432173848460235,\n \
\ \"acc_norm\": 0.6410256410256411,\n \"acc_norm_stderr\": 0.02432173848460235\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.3296296296296296,\n \"acc_stderr\": 0.028661201116524572,\n \
\ \"acc_norm\": 0.3296296296296296,\n \"acc_norm_stderr\": 0.028661201116524572\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6974789915966386,\n \"acc_stderr\": 0.02983796238829194,\n \
\ \"acc_norm\": 0.6974789915966386,\n \"acc_norm_stderr\": 0.02983796238829194\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.40397350993377484,\n \"acc_stderr\": 0.04006485685365342,\n \"\
acc_norm\": 0.40397350993377484,\n \"acc_norm_stderr\": 0.04006485685365342\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8348623853211009,\n \"acc_stderr\": 0.015919557829976044,\n \"\
acc_norm\": 0.8348623853211009,\n \"acc_norm_stderr\": 0.015919557829976044\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5046296296296297,\n \"acc_stderr\": 0.03409825519163572,\n \"\
acc_norm\": 0.5046296296296297,\n \"acc_norm_stderr\": 0.03409825519163572\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8235294117647058,\n \"acc_stderr\": 0.026756401538078966,\n \"\
acc_norm\": 0.8235294117647058,\n \"acc_norm_stderr\": 0.026756401538078966\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7721518987341772,\n \"acc_stderr\": 0.027303484599069422,\n \
\ \"acc_norm\": 0.7721518987341772,\n \"acc_norm_stderr\": 0.027303484599069422\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6816143497757847,\n\
\ \"acc_stderr\": 0.03126580522513713,\n \"acc_norm\": 0.6816143497757847,\n\
\ \"acc_norm_stderr\": 0.03126580522513713\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7862595419847328,\n \"acc_stderr\": 0.0359546161177469,\n\
\ \"acc_norm\": 0.7862595419847328,\n \"acc_norm_stderr\": 0.0359546161177469\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.768595041322314,\n \"acc_stderr\": 0.03849856098794088,\n \"acc_norm\"\
: 0.768595041322314,\n \"acc_norm_stderr\": 0.03849856098794088\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\": 0.7852760736196319,\n \"acc_stderr\": 0.03226219377286775,\n\
\ \"acc_norm\": 0.7852760736196319,\n \"acc_norm_stderr\": 0.03226219377286775\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.49107142857142855,\n\
\ \"acc_stderr\": 0.04745033255489123,\n \"acc_norm\": 0.49107142857142855,\n\
\ \"acc_norm_stderr\": 0.04745033255489123\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8155339805825242,\n \"acc_stderr\": 0.03840423627288276,\n\
\ \"acc_norm\": 0.8155339805825242,\n \"acc_norm_stderr\": 0.03840423627288276\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8846153846153846,\n\
\ \"acc_stderr\": 0.02093019318517933,\n \"acc_norm\": 0.8846153846153846,\n\
\ \"acc_norm_stderr\": 0.02093019318517933\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.72,\n \"acc_stderr\": 0.045126085985421276,\n \
\ \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.045126085985421276\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8301404853128991,\n\
\ \"acc_stderr\": 0.013428186370608318,\n \"acc_norm\": 0.8301404853128991,\n\
\ \"acc_norm_stderr\": 0.013428186370608318\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7312138728323699,\n \"acc_stderr\": 0.023868003262500107,\n\
\ \"acc_norm\": 0.7312138728323699,\n \"acc_norm_stderr\": 0.023868003262500107\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.25921787709497207,\n\
\ \"acc_stderr\": 0.014655780837497724,\n \"acc_norm\": 0.25921787709497207,\n\
\ \"acc_norm_stderr\": 0.014655780837497724\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7222222222222222,\n \"acc_stderr\": 0.0256468630971379,\n\
\ \"acc_norm\": 0.7222222222222222,\n \"acc_norm_stderr\": 0.0256468630971379\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.684887459807074,\n\
\ \"acc_stderr\": 0.02638527370346449,\n \"acc_norm\": 0.684887459807074,\n\
\ \"acc_norm_stderr\": 0.02638527370346449\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7098765432098766,\n \"acc_stderr\": 0.025251173936495036,\n\
\ \"acc_norm\": 0.7098765432098766,\n \"acc_norm_stderr\": 0.025251173936495036\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.4858156028368794,\n \"acc_stderr\": 0.02981549448368206,\n \
\ \"acc_norm\": 0.4858156028368794,\n \"acc_norm_stderr\": 0.02981549448368206\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.45371577574967403,\n\
\ \"acc_stderr\": 0.012715404841277738,\n \"acc_norm\": 0.45371577574967403,\n\
\ \"acc_norm_stderr\": 0.012715404841277738\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6838235294117647,\n \"acc_stderr\": 0.028245687391462937,\n\
\ \"acc_norm\": 0.6838235294117647,\n \"acc_norm_stderr\": 0.028245687391462937\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6519607843137255,\n \"acc_stderr\": 0.019270998708223977,\n \
\ \"acc_norm\": 0.6519607843137255,\n \"acc_norm_stderr\": 0.019270998708223977\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6545454545454545,\n\
\ \"acc_stderr\": 0.04554619617541054,\n \"acc_norm\": 0.6545454545454545,\n\
\ \"acc_norm_stderr\": 0.04554619617541054\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7346938775510204,\n \"acc_stderr\": 0.028263889943784586,\n\
\ \"acc_norm\": 0.7346938775510204,\n \"acc_norm_stderr\": 0.028263889943784586\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.835820895522388,\n\
\ \"acc_stderr\": 0.026193923544454132,\n \"acc_norm\": 0.835820895522388,\n\
\ \"acc_norm_stderr\": 0.026193923544454132\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.87,\n \"acc_stderr\": 0.033799766898963086,\n \
\ \"acc_norm\": 0.87,\n \"acc_norm_stderr\": 0.033799766898963086\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\": 0.8128654970760234,\n \"acc_stderr\": 0.02991312723236804,\n\
\ \"acc_norm\": 0.8128654970760234,\n \"acc_norm_stderr\": 0.02991312723236804\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3157894736842105,\n\
\ \"mc1_stderr\": 0.016272287957916916,\n \"mc2\": 0.4605602132329784,\n\
\ \"mc2_stderr\": 0.014970488994464466\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7758484609313339,\n \"acc_stderr\": 0.011720400740774092\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.5579984836997726,\n \
\ \"acc_stderr\": 0.01367951449281457\n }\n}\n```"
repo_url: https://huggingface.co/DreadPoor/ToppyEvil-7B-slerp
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_01T18_32_26.393415
path:
- '**/details_harness|arc:challenge|25_2024-02-01T18-32-26.393415.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-02-01T18-32-26.393415.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_02_01T18_32_26.393415
path:
- '**/details_harness|gsm8k|5_2024-02-01T18-32-26.393415.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-02-01T18-32-26.393415.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_02_01T18_32_26.393415
path:
- '**/details_harness|hellaswag|10_2024-02-01T18-32-26.393415.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-02-01T18-32-26.393415.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_02_01T18_32_26.393415
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-01T18-32-26.393415.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-01T18-32-26.393415.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-01T18-32-26.393415.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_02_01T18_32_26.393415
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-01T18-32-26.393415.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-01T18-32-26.393415.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_02_01T18_32_26.393415
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-01T18-32-26.393415.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-01T18-32-26.393415.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_02_01T18_32_26.393415
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-01T18-32-26.393415.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-01T18-32-26.393415.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_02_01T18_32_26.393415
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-01T18-32-26.393415.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-01T18-32-26.393415.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_02_01T18_32_26.393415
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-01T18-32-26.393415.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-01T18-32-26.393415.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_02_01T18_32_26.393415
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-01T18-32-26.393415.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-01T18-32-26.393415.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_02_01T18_32_26.393415
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-01T18-32-26.393415.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-01T18-32-26.393415.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_02_01T18_32_26.393415
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-01T18-32-26.393415.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-01T18-32-26.393415.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_02_01T18_32_26.393415
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-01T18-32-26.393415.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-01T18-32-26.393415.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_02_01T18_32_26.393415
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-01T18-32-26.393415.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-01T18-32-26.393415.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_02_01T18_32_26.393415
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-01T18-32-26.393415.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-01T18-32-26.393415.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_02_01T18_32_26.393415
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-01T18-32-26.393415.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-01T18-32-26.393415.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_02_01T18_32_26.393415
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-01T18-32-26.393415.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-01T18-32-26.393415.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_02_01T18_32_26.393415
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-01T18-32-26.393415.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-01T18-32-26.393415.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_02_01T18_32_26.393415
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-01T18-32-26.393415.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-01T18-32-26.393415.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_02_01T18_32_26.393415
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-01T18-32-26.393415.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-01T18-32-26.393415.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_02_01T18_32_26.393415
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-01T18-32-26.393415.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-01T18-32-26.393415.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_02_01T18_32_26.393415
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-01T18-32-26.393415.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-01T18-32-26.393415.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_02_01T18_32_26.393415
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-01T18-32-26.393415.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-01T18-32-26.393415.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_02_01T18_32_26.393415
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-01T18-32-26.393415.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-01T18-32-26.393415.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_02_01T18_32_26.393415
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-01T18-32-26.393415.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-01T18-32-26.393415.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_02_01T18_32_26.393415
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-01T18-32-26.393415.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-01T18-32-26.393415.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_02_01T18_32_26.393415
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-01T18-32-26.393415.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-01T18-32-26.393415.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_02_01T18_32_26.393415
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-01T18-32-26.393415.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-01T18-32-26.393415.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_02_01T18_32_26.393415
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-01T18-32-26.393415.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-01T18-32-26.393415.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_02_01T18_32_26.393415
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-01T18-32-26.393415.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-01T18-32-26.393415.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_02_01T18_32_26.393415
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-01T18-32-26.393415.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-01T18-32-26.393415.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_02_01T18_32_26.393415
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-01T18-32-26.393415.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-01T18-32-26.393415.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_02_01T18_32_26.393415
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-01T18-32-26.393415.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-01T18-32-26.393415.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_02_01T18_32_26.393415
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-01T18-32-26.393415.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-01T18-32-26.393415.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_02_01T18_32_26.393415
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-01T18-32-26.393415.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-01T18-32-26.393415.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_02_01T18_32_26.393415
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-01T18-32-26.393415.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-01T18-32-26.393415.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_02_01T18_32_26.393415
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-01T18-32-26.393415.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-01T18-32-26.393415.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_02_01T18_32_26.393415
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-01T18-32-26.393415.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-01T18-32-26.393415.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_02_01T18_32_26.393415
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-01T18-32-26.393415.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-01T18-32-26.393415.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_02_01T18_32_26.393415
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-01T18-32-26.393415.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-01T18-32-26.393415.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_02_01T18_32_26.393415
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-01T18-32-26.393415.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-01T18-32-26.393415.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_02_01T18_32_26.393415
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-01T18-32-26.393415.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-01T18-32-26.393415.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_02_01T18_32_26.393415
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-01T18-32-26.393415.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-01T18-32-26.393415.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_02_01T18_32_26.393415
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-01T18-32-26.393415.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-01T18-32-26.393415.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_02_01T18_32_26.393415
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-01T18-32-26.393415.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-01T18-32-26.393415.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_02_01T18_32_26.393415
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-01T18-32-26.393415.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-01T18-32-26.393415.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_02_01T18_32_26.393415
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-01T18-32-26.393415.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-01T18-32-26.393415.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_02_01T18_32_26.393415
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-01T18-32-26.393415.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-01T18-32-26.393415.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_02_01T18_32_26.393415
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-01T18-32-26.393415.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-01T18-32-26.393415.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_02_01T18_32_26.393415
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-01T18-32-26.393415.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-01T18-32-26.393415.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_02_01T18_32_26.393415
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-01T18-32-26.393415.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-01T18-32-26.393415.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_02_01T18_32_26.393415
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-01T18-32-26.393415.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-01T18-32-26.393415.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_02_01T18_32_26.393415
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-01T18-32-26.393415.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-01T18-32-26.393415.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_02_01T18_32_26.393415
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-01T18-32-26.393415.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-01T18-32-26.393415.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_02_01T18_32_26.393415
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-01T18-32-26.393415.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-01T18-32-26.393415.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_02_01T18_32_26.393415
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-01T18-32-26.393415.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-01T18-32-26.393415.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_02_01T18_32_26.393415
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-01T18-32-26.393415.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-01T18-32-26.393415.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_02_01T18_32_26.393415
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-01T18-32-26.393415.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-01T18-32-26.393415.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_02_01T18_32_26.393415
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-01T18-32-26.393415.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-01T18-32-26.393415.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_02_01T18_32_26.393415
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-01T18-32-26.393415.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-01T18-32-26.393415.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_02_01T18_32_26.393415
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-01T18-32-26.393415.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-01T18-32-26.393415.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_02_01T18_32_26.393415
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-01T18-32-26.393415.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-01T18-32-26.393415.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_02_01T18_32_26.393415
path:
- '**/details_harness|winogrande|5_2024-02-01T18-32-26.393415.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-02-01T18-32-26.393415.parquet'
- config_name: results
data_files:
- split: 2024_02_01T18_32_26.393415
path:
- results_2024-02-01T18-32-26.393415.parquet
- split: latest
path:
- results_2024-02-01T18-32-26.393415.parquet
---
# Dataset Card for Evaluation run of DreadPoor/ToppyEvil-7B-slerp
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [DreadPoor/ToppyEvil-7B-slerp](https://huggingface.co/DreadPoor/ToppyEvil-7B-slerp) 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_DreadPoor__ToppyEvil-7B-slerp",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-02-01T18:32:26.393415](https://huggingface.co/datasets/open-llm-leaderboard/details_DreadPoor__ToppyEvil-7B-slerp/blob/main/results_2024-02-01T18-32-26.393415.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": 0.6371495869198338,
"acc_stderr": 0.03235698893021149,
"acc_norm": 0.6394933656367955,
"acc_norm_stderr": 0.03300210838103083,
"mc1": 0.3157894736842105,
"mc1_stderr": 0.016272287957916916,
"mc2": 0.4605602132329784,
"mc2_stderr": 0.014970488994464466
},
"harness|arc:challenge|25": {
"acc": 0.6100682593856656,
"acc_stderr": 0.014252959848892893,
"acc_norm": 0.636518771331058,
"acc_norm_stderr": 0.014056207319068283
},
"harness|hellaswag|10": {
"acc": 0.6714797849034057,
"acc_stderr": 0.00468715199479107,
"acc_norm": 0.8428599880501892,
"acc_norm_stderr": 0.00363188949612254
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.39,
"acc_stderr": 0.04902071300001975,
"acc_norm": 0.39,
"acc_norm_stderr": 0.04902071300001975
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.562962962962963,
"acc_stderr": 0.042849586397534015,
"acc_norm": 0.562962962962963,
"acc_norm_stderr": 0.042849586397534015
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6842105263157895,
"acc_stderr": 0.03782728980865469,
"acc_norm": 0.6842105263157895,
"acc_norm_stderr": 0.03782728980865469
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.58,
"acc_stderr": 0.049604496374885836,
"acc_norm": 0.58,
"acc_norm_stderr": 0.049604496374885836
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6792452830188679,
"acc_stderr": 0.028727502957880267,
"acc_norm": 0.6792452830188679,
"acc_norm_stderr": 0.028727502957880267
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7361111111111112,
"acc_stderr": 0.03685651095897532,
"acc_norm": 0.7361111111111112,
"acc_norm_stderr": 0.03685651095897532
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.46,
"acc_stderr": 0.05009082659620332,
"acc_norm": 0.46,
"acc_norm_stderr": 0.05009082659620332
},
"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": 0.3,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.3,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6705202312138728,
"acc_stderr": 0.03583901754736412,
"acc_norm": 0.6705202312138728,
"acc_norm_stderr": 0.03583901754736412
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.37254901960784315,
"acc_stderr": 0.04810840148082635,
"acc_norm": 0.37254901960784315,
"acc_norm_stderr": 0.04810840148082635
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.77,
"acc_stderr": 0.04229525846816508,
"acc_norm": 0.77,
"acc_norm_stderr": 0.04229525846816508
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5361702127659574,
"acc_stderr": 0.03260038511835771,
"acc_norm": 0.5361702127659574,
"acc_norm_stderr": 0.03260038511835771
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.5087719298245614,
"acc_stderr": 0.04702880432049615,
"acc_norm": 0.5087719298245614,
"acc_norm_stderr": 0.04702880432049615
},
"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": 0.43915343915343913,
"acc_stderr": 0.025559920550531003,
"acc_norm": 0.43915343915343913,
"acc_norm_stderr": 0.025559920550531003
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.4365079365079365,
"acc_stderr": 0.04435932892851466,
"acc_norm": 0.4365079365079365,
"acc_norm_stderr": 0.04435932892851466
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.37,
"acc_stderr": 0.04852365870939099,
"acc_norm": 0.37,
"acc_norm_stderr": 0.04852365870939099
},
"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": 0.4876847290640394,
"acc_stderr": 0.035169204442208966,
"acc_norm": 0.4876847290640394,
"acc_norm_stderr": 0.035169204442208966
},
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"acc": 0.5579984836997726,
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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
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### Out-of-Scope Use
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## Dataset Structure
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## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
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### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
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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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#### 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]
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Cohere/miracl-ko-corpus-22-12 | ---
annotations_creators:
- expert-generated
language:
- ko
multilinguality:
- multilingual
size_categories: []
source_datasets: []
tags: []
task_categories:
- text-retrieval
license:
- apache-2.0
task_ids:
- document-retrieval
---
# MIRACL (ko) embedded with cohere.ai `multilingual-22-12` encoder
We encoded the [MIRACL dataset](https://huggingface.co/miracl) using the [cohere.ai](https://txt.cohere.ai/multilingual/) `multilingual-22-12` embedding model.
The query embeddings can be found in [Cohere/miracl-ko-queries-22-12](https://huggingface.co/datasets/Cohere/miracl-ko-queries-22-12) and the corpus embeddings can be found in [Cohere/miracl-ko-corpus-22-12](https://huggingface.co/datasets/Cohere/miracl-ko-corpus-22-12).
For the orginal datasets, see [miracl/miracl](https://huggingface.co/datasets/miracl/miracl) and [miracl/miracl-corpus](https://huggingface.co/datasets/miracl/miracl-corpus).
Dataset info:
> MIRACL 🌍🙌🌏 (Multilingual Information Retrieval Across a Continuum of Languages) is a multilingual retrieval dataset that focuses on search across 18 different languages, which collectively encompass over three billion native speakers around the world.
>
> The corpus for each language is prepared from a Wikipedia dump, where we keep only the plain text and discard images, tables, etc. Each article is segmented into multiple passages using WikiExtractor based on natural discourse units (e.g., `\n\n` in the wiki markup). Each of these passages comprises a "document" or unit of retrieval. We preserve the Wikipedia article title of each passage.
## Embeddings
We compute for `title+" "+text` the embeddings using our `multilingual-22-12` embedding model, a state-of-the-art model that works for semantic search in 100 languages. If you want to learn more about this model, have a look at [cohere.ai multilingual embedding model](https://txt.cohere.ai/multilingual/).
## Loading the dataset
In [miracl-ko-corpus-22-12](https://huggingface.co/datasets/Cohere/miracl-ko-corpus-22-12) we provide the corpus embeddings. Note, depending on the selected split, the respective files can be quite large.
You can either load the dataset like this:
```python
from datasets import load_dataset
docs = load_dataset(f"Cohere/miracl-ko-corpus-22-12", split="train")
```
Or you can also stream it without downloading it before:
```python
from datasets import load_dataset
docs = load_dataset(f"Cohere/miracl-ko-corpus-22-12", split="train", streaming=True)
for doc in docs:
docid = doc['docid']
title = doc['title']
text = doc['text']
emb = doc['emb']
```
## Search
Have a look at [miracl-ko-queries-22-12](https://huggingface.co/datasets/Cohere/miracl-ko-queries-22-12) where we provide the query embeddings for the MIRACL dataset.
To search in the documents, you must use **dot-product**.
And then compare this query embeddings either with a vector database (recommended) or directly computing the dot product.
A full search example:
```python
# Attention! For large datasets, this requires a lot of memory to store
# all document embeddings and to compute the dot product scores.
# Only use this for smaller datasets. For large datasets, use a vector DB
from datasets import load_dataset
import torch
#Load documents + embeddings
docs = load_dataset(f"Cohere/miracl-ko-corpus-22-12", split="train")
doc_embeddings = torch.tensor(docs['emb'])
# Load queries
queries = load_dataset(f"Cohere/miracl-ko-queries-22-12", split="dev")
# Select the first query as example
qid = 0
query = queries[qid]
query_embedding = torch.tensor(queries['emb'])
# Compute dot score between query embedding and document embeddings
dot_scores = torch.mm(query_embedding, doc_embeddings.transpose(0, 1))
top_k = torch.topk(dot_scores, k=3)
# Print results
print("Query:", query['query'])
for doc_id in top_k.indices[0].tolist():
print(docs[doc_id]['title'])
print(docs[doc_id]['text'])
```
You can get embeddings for new queries using our API:
```python
#Run: pip install cohere
import cohere
co = cohere.Client(f"{api_key}") # You should add your cohere API Key here :))
texts = ['my search query']
response = co.embed(texts=texts, model='multilingual-22-12')
query_embedding = response.embeddings[0] # Get the embedding for the first text
```
## Performance
In the following table we compare the cohere multilingual-22-12 model with Elasticsearch version 8.6.0 lexical search (title and passage indexed as independent fields). Note that Elasticsearch doesn't support all languages that are part of the MIRACL dataset.
We compute nDCG@10 (a ranking based loss), as well as hit@3: Is at least one relevant document in the top-3 results. We find that hit@3 is easier to interpret, as it presents the number of queries for which a relevant document is found among the top-3 results.
Note: MIRACL only annotated a small fraction of passages (10 per query) for relevancy. Especially for larger Wikipedias (like English), we often found many more relevant passages. This is know as annotation holes. Real nDCG@10 and hit@3 performance is likely higher than depicted.
| Model | cohere multilingual-22-12 nDCG@10 | cohere multilingual-22-12 hit@3 | ES 8.6.0 nDCG@10 | ES 8.6.0 acc@3 |
|---|---|---|---|---|
| miracl-ar | 64.2 | 75.2 | 46.8 | 56.2 |
| miracl-bn | 61.5 | 75.7 | 49.2 | 60.1 |
| miracl-de | 44.4 | 60.7 | 19.6 | 29.8 |
| miracl-en | 44.6 | 62.2 | 30.2 | 43.2 |
| miracl-es | 47.0 | 74.1 | 27.0 | 47.2 |
| miracl-fi | 63.7 | 76.2 | 51.4 | 61.6 |
| miracl-fr | 46.8 | 57.1 | 17.0 | 21.6 |
| miracl-hi | 50.7 | 62.9 | 41.0 | 48.9 |
| miracl-id | 44.8 | 63.8 | 39.2 | 54.7 |
| miracl-ru | 49.2 | 66.9 | 25.4 | 36.7 |
| **Avg** | 51.7 | 67.5 | 34.7 | 46.0 |
Further languages (not supported by Elasticsearch):
| Model | cohere multilingual-22-12 nDCG@10 | cohere multilingual-22-12 hit@3 |
|---|---|---|
| miracl-fa | 44.8 | 53.6 |
| miracl-ja | 49.0 | 61.0 |
| miracl-ko | 50.9 | 64.8 |
| miracl-sw | 61.4 | 74.5 |
| miracl-te | 67.8 | 72.3 |
| miracl-th | 60.2 | 71.9 |
| miracl-yo | 56.4 | 62.2 |
| miracl-zh | 43.8 | 56.5 |
| **Avg** | 54.3 | 64.6 |
|
hac541309/polyglot-ko-tokenizer-corpus | ---
language: ko
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 17909910180
num_examples: 11808255
download_size: 9384042407
dataset_size: 17909910180
---
# Dataset Card for "polyglot-ko-tokenizer-corpus"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
HuggingFaceM4/ROBUT-sqa-rendered-tables | Invalid username or password. |
Seongill/squad_missing_answer | ---
dataset_info:
features:
- name: id
dtype: string
- name: title
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answers
struct:
- name: answer_start
sequence: int64
- name: text
sequence: string
- name: masked_query
dtype: string
- name: query_embedding
sequence: float64
- name: random_answer
dtype: string
- name: similar_answer
dtype: string
- name: similar_answer_v2
dtype: string
- name: answer
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 923830110
num_examples: 97748
download_size: 591722376
dataset_size: 923830110
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
akash140500/mini-platypus | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 4186564
num_examples: 1000
download_size: 2245924
dataset_size: 4186564
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/ae21281c | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 184
num_examples: 10
download_size: 1339
dataset_size: 184
---
# Dataset Card for "ae21281c"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_wang7776__Llama-2-7b-chat-hf-10-attention-sparsity | ---
pretty_name: Evaluation run of wang7776/Llama-2-7b-chat-hf-10-attention-sparsity
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [wang7776/Llama-2-7b-chat-hf-10-attention-sparsity](https://huggingface.co/wang7776/Llama-2-7b-chat-hf-10-attention-sparsity)\
\ 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_wang7776__Llama-2-7b-chat-hf-10-attention-sparsity\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-01-26T21:34:29.801410](https://huggingface.co/datasets/open-llm-leaderboard/details_wang7776__Llama-2-7b-chat-hf-10-attention-sparsity/blob/main/results_2024-01-26T21-34-29.801410.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\": 0.48218747214367264,\n\
\ \"acc_stderr\": 0.03435843254658187,\n \"acc_norm\": 0.4868926638618298,\n\
\ \"acc_norm_stderr\": 0.03511073988628273,\n \"mc1\": 0.30354957160342716,\n\
\ \"mc1_stderr\": 0.016095884155386847,\n \"mc2\": 0.4540163852731679,\n\
\ \"mc2_stderr\": 0.0157382073149144\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.4991467576791809,\n \"acc_stderr\": 0.014611369529813276,\n\
\ \"acc_norm\": 0.5290102389078498,\n \"acc_norm_stderr\": 0.014586776355294321\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5931089424417447,\n\
\ \"acc_stderr\": 0.004902502514738599,\n \"acc_norm\": 0.7818163712407887,\n\
\ \"acc_norm_stderr\": 0.0041216867002386\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542129,\n \
\ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542129\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.4148148148148148,\n\
\ \"acc_stderr\": 0.042561937679014075,\n \"acc_norm\": 0.4148148148148148,\n\
\ \"acc_norm_stderr\": 0.042561937679014075\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.47368421052631576,\n \"acc_stderr\": 0.04063302731486671,\n\
\ \"acc_norm\": 0.47368421052631576,\n \"acc_norm_stderr\": 0.04063302731486671\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.5,\n\
\ \"acc_stderr\": 0.050251890762960605,\n \"acc_norm\": 0.5,\n \
\ \"acc_norm_stderr\": 0.050251890762960605\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.539622641509434,\n \"acc_stderr\": 0.03067609659938918,\n\
\ \"acc_norm\": 0.539622641509434,\n \"acc_norm_stderr\": 0.03067609659938918\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.5069444444444444,\n\
\ \"acc_stderr\": 0.04180806750294938,\n \"acc_norm\": 0.5069444444444444,\n\
\ \"acc_norm_stderr\": 0.04180806750294938\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542127,\n \
\ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542127\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.38,\n \"acc_stderr\": 0.048783173121456316,\n \"acc_norm\": 0.38,\n\
\ \"acc_norm_stderr\": 0.048783173121456316\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939098,\n \
\ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939098\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.3930635838150289,\n\
\ \"acc_stderr\": 0.03724249595817731,\n \"acc_norm\": 0.3930635838150289,\n\
\ \"acc_norm_stderr\": 0.03724249595817731\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.23529411764705882,\n \"acc_stderr\": 0.04220773659171453,\n\
\ \"acc_norm\": 0.23529411764705882,\n \"acc_norm_stderr\": 0.04220773659171453\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.56,\n \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\": 0.56,\n\
\ \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.4127659574468085,\n \"acc_stderr\": 0.03218471141400351,\n\
\ \"acc_norm\": 0.4127659574468085,\n \"acc_norm_stderr\": 0.03218471141400351\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.37719298245614036,\n\
\ \"acc_stderr\": 0.045595221419582166,\n \"acc_norm\": 0.37719298245614036,\n\
\ \"acc_norm_stderr\": 0.045595221419582166\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.4689655172413793,\n \"acc_stderr\": 0.04158632762097828,\n\
\ \"acc_norm\": 0.4689655172413793,\n \"acc_norm_stderr\": 0.04158632762097828\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.30158730158730157,\n \"acc_stderr\": 0.023636975996101806,\n \"\
acc_norm\": 0.30158730158730157,\n \"acc_norm_stderr\": 0.023636975996101806\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.2619047619047619,\n\
\ \"acc_stderr\": 0.0393253768039287,\n \"acc_norm\": 0.2619047619047619,\n\
\ \"acc_norm_stderr\": 0.0393253768039287\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \
\ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.5193548387096775,\n\
\ \"acc_stderr\": 0.028422687404312107,\n \"acc_norm\": 0.5193548387096775,\n\
\ \"acc_norm_stderr\": 0.028422687404312107\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.35467980295566504,\n \"acc_stderr\": 0.0336612448905145,\n\
\ \"acc_norm\": 0.35467980295566504,\n \"acc_norm_stderr\": 0.0336612448905145\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.41,\n \"acc_stderr\": 0.04943110704237102,\n \"acc_norm\"\
: 0.41,\n \"acc_norm_stderr\": 0.04943110704237102\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.6121212121212121,\n \"acc_stderr\": 0.038049136539710114,\n\
\ \"acc_norm\": 0.6121212121212121,\n \"acc_norm_stderr\": 0.038049136539710114\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.5959595959595959,\n \"acc_stderr\": 0.03496130972056128,\n \"\
acc_norm\": 0.5959595959595959,\n \"acc_norm_stderr\": 0.03496130972056128\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.6839378238341969,\n \"acc_stderr\": 0.033553973696861736,\n\
\ \"acc_norm\": 0.6839378238341969,\n \"acc_norm_stderr\": 0.033553973696861736\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.4230769230769231,\n \"acc_stderr\": 0.025049197876042338,\n\
\ \"acc_norm\": 0.4230769230769231,\n \"acc_norm_stderr\": 0.025049197876042338\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.25925925925925924,\n \"acc_stderr\": 0.026719240783712177,\n \
\ \"acc_norm\": 0.25925925925925924,\n \"acc_norm_stderr\": 0.026719240783712177\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.42857142857142855,\n \"acc_stderr\": 0.032145368597886394,\n\
\ \"acc_norm\": 0.42857142857142855,\n \"acc_norm_stderr\": 0.032145368597886394\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.2980132450331126,\n \"acc_stderr\": 0.037345356767871984,\n \"\
acc_norm\": 0.2980132450331126,\n \"acc_norm_stderr\": 0.037345356767871984\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.6623853211009174,\n \"acc_stderr\": 0.02027526598663892,\n \"\
acc_norm\": 0.6623853211009174,\n \"acc_norm_stderr\": 0.02027526598663892\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.32407407407407407,\n \"acc_stderr\": 0.03191923445686185,\n \"\
acc_norm\": 0.32407407407407407,\n \"acc_norm_stderr\": 0.03191923445686185\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.6568627450980392,\n \"acc_stderr\": 0.03332139944668085,\n \"\
acc_norm\": 0.6568627450980392,\n \"acc_norm_stderr\": 0.03332139944668085\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.6582278481012658,\n \"acc_stderr\": 0.03087453753755362,\n \
\ \"acc_norm\": 0.6582278481012658,\n \"acc_norm_stderr\": 0.03087453753755362\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.5650224215246636,\n\
\ \"acc_stderr\": 0.033272833702713445,\n \"acc_norm\": 0.5650224215246636,\n\
\ \"acc_norm_stderr\": 0.033272833702713445\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.5648854961832062,\n \"acc_stderr\": 0.04348208051644858,\n\
\ \"acc_norm\": 0.5648854961832062,\n \"acc_norm_stderr\": 0.04348208051644858\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.6363636363636364,\n \"acc_stderr\": 0.043913262867240704,\n \"\
acc_norm\": 0.6363636363636364,\n \"acc_norm_stderr\": 0.043913262867240704\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.5833333333333334,\n\
\ \"acc_stderr\": 0.04766075165356461,\n \"acc_norm\": 0.5833333333333334,\n\
\ \"acc_norm_stderr\": 0.04766075165356461\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.5644171779141104,\n \"acc_stderr\": 0.03895632464138937,\n\
\ \"acc_norm\": 0.5644171779141104,\n \"acc_norm_stderr\": 0.03895632464138937\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.33035714285714285,\n\
\ \"acc_stderr\": 0.04464285714285713,\n \"acc_norm\": 0.33035714285714285,\n\
\ \"acc_norm_stderr\": 0.04464285714285713\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.6990291262135923,\n \"acc_stderr\": 0.045416094465039476,\n\
\ \"acc_norm\": 0.6990291262135923,\n \"acc_norm_stderr\": 0.045416094465039476\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.7222222222222222,\n\
\ \"acc_stderr\": 0.02934311479809446,\n \"acc_norm\": 0.7222222222222222,\n\
\ \"acc_norm_stderr\": 0.02934311479809446\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956911,\n \
\ \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956911\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.6794380587484036,\n\
\ \"acc_stderr\": 0.016688893310803768,\n \"acc_norm\": 0.6794380587484036,\n\
\ \"acc_norm_stderr\": 0.016688893310803768\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.5115606936416185,\n \"acc_stderr\": 0.026911898686377927,\n\
\ \"acc_norm\": 0.5115606936416185,\n \"acc_norm_stderr\": 0.026911898686377927\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.23016759776536314,\n\
\ \"acc_stderr\": 0.014078339253425812,\n \"acc_norm\": 0.23016759776536314,\n\
\ \"acc_norm_stderr\": 0.014078339253425812\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.5228758169934641,\n \"acc_stderr\": 0.028599936776089782,\n\
\ \"acc_norm\": 0.5228758169934641,\n \"acc_norm_stderr\": 0.028599936776089782\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.5691318327974276,\n\
\ \"acc_stderr\": 0.028125340983972714,\n \"acc_norm\": 0.5691318327974276,\n\
\ \"acc_norm_stderr\": 0.028125340983972714\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.5524691358024691,\n \"acc_stderr\": 0.0276671385694227,\n\
\ \"acc_norm\": 0.5524691358024691,\n \"acc_norm_stderr\": 0.0276671385694227\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.37943262411347517,\n \"acc_stderr\": 0.028947338851614105,\n \
\ \"acc_norm\": 0.37943262411347517,\n \"acc_norm_stderr\": 0.028947338851614105\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.3474576271186441,\n\
\ \"acc_stderr\": 0.0121614177297498,\n \"acc_norm\": 0.3474576271186441,\n\
\ \"acc_norm_stderr\": 0.0121614177297498\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.45955882352941174,\n \"acc_stderr\": 0.03027332507734576,\n\
\ \"acc_norm\": 0.45955882352941174,\n \"acc_norm_stderr\": 0.03027332507734576\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.4722222222222222,\n \"acc_stderr\": 0.02019659493354119,\n \
\ \"acc_norm\": 0.4722222222222222,\n \"acc_norm_stderr\": 0.02019659493354119\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.5363636363636364,\n\
\ \"acc_stderr\": 0.04776449162396197,\n \"acc_norm\": 0.5363636363636364,\n\
\ \"acc_norm_stderr\": 0.04776449162396197\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.5183673469387755,\n \"acc_stderr\": 0.03198761546763127,\n\
\ \"acc_norm\": 0.5183673469387755,\n \"acc_norm_stderr\": 0.03198761546763127\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.6417910447761194,\n\
\ \"acc_stderr\": 0.03390393042268815,\n \"acc_norm\": 0.6417910447761194,\n\
\ \"acc_norm_stderr\": 0.03390393042268815\n },\n \"harness|hendrycksTest-us_foreign_policy|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-virology|5\": {\n \"acc\": 0.42771084337349397,\n\
\ \"acc_stderr\": 0.038515976837185335,\n \"acc_norm\": 0.42771084337349397,\n\
\ \"acc_norm_stderr\": 0.038515976837185335\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.7192982456140351,\n \"acc_stderr\": 0.03446296217088427,\n\
\ \"acc_norm\": 0.7192982456140351,\n \"acc_norm_stderr\": 0.03446296217088427\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.30354957160342716,\n\
\ \"mc1_stderr\": 0.016095884155386847,\n \"mc2\": 0.4540163852731679,\n\
\ \"mc2_stderr\": 0.0157382073149144\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7142857142857143,\n \"acc_stderr\": 0.012696531870038616\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.1910538286580743,\n \
\ \"acc_stderr\": 0.01082879119175519\n }\n}\n```"
repo_url: https://huggingface.co/wang7776/Llama-2-7b-chat-hf-10-attention-sparsity
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_01_26T21_34_29.801410
path:
- '**/details_harness|arc:challenge|25_2024-01-26T21-34-29.801410.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-01-26T21-34-29.801410.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_01_26T21_34_29.801410
path:
- '**/details_harness|gsm8k|5_2024-01-26T21-34-29.801410.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-01-26T21-34-29.801410.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_01_26T21_34_29.801410
path:
- '**/details_harness|hellaswag|10_2024-01-26T21-34-29.801410.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-01-26T21-34-29.801410.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_01_26T21_34_29.801410
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-26T21-34-29.801410.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-26T21-34-29.801410.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-26T21-34-29.801410.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_01_26T21_34_29.801410
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-26T21-34-29.801410.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-26T21-34-29.801410.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_01_26T21_34_29.801410
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-26T21-34-29.801410.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-26T21-34-29.801410.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_01_26T21_34_29.801410
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-26T21-34-29.801410.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-26T21-34-29.801410.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_01_26T21_34_29.801410
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-26T21-34-29.801410.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-26T21-34-29.801410.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_01_26T21_34_29.801410
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-26T21-34-29.801410.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-26T21-34-29.801410.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_01_26T21_34_29.801410
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-26T21-34-29.801410.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-26T21-34-29.801410.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_01_26T21_34_29.801410
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-26T21-34-29.801410.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-26T21-34-29.801410.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_01_26T21_34_29.801410
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-26T21-34-29.801410.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-26T21-34-29.801410.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_01_26T21_34_29.801410
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-26T21-34-29.801410.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-26T21-34-29.801410.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_01_26T21_34_29.801410
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-26T21-34-29.801410.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-26T21-34-29.801410.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_01_26T21_34_29.801410
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-26T21-34-29.801410.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-26T21-34-29.801410.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_01_26T21_34_29.801410
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-26T21-34-29.801410.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-26T21-34-29.801410.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_01_26T21_34_29.801410
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-26T21-34-29.801410.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-26T21-34-29.801410.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_01_26T21_34_29.801410
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-26T21-34-29.801410.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-26T21-34-29.801410.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_01_26T21_34_29.801410
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-26T21-34-29.801410.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-26T21-34-29.801410.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_01_26T21_34_29.801410
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-26T21-34-29.801410.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-26T21-34-29.801410.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_01_26T21_34_29.801410
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-26T21-34-29.801410.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-26T21-34-29.801410.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_01_26T21_34_29.801410
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-26T21-34-29.801410.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-26T21-34-29.801410.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_01_26T21_34_29.801410
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-26T21-34-29.801410.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-26T21-34-29.801410.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_01_26T21_34_29.801410
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-26T21-34-29.801410.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-26T21-34-29.801410.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_01_26T21_34_29.801410
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-26T21-34-29.801410.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-26T21-34-29.801410.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_01_26T21_34_29.801410
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-26T21-34-29.801410.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-26T21-34-29.801410.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_01_26T21_34_29.801410
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-26T21-34-29.801410.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-26T21-34-29.801410.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_01_26T21_34_29.801410
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-26T21-34-29.801410.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-26T21-34-29.801410.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_01_26T21_34_29.801410
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-26T21-34-29.801410.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-26T21-34-29.801410.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_01_26T21_34_29.801410
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-26T21-34-29.801410.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-26T21-34-29.801410.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_01_26T21_34_29.801410
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-26T21-34-29.801410.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-26T21-34-29.801410.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_01_26T21_34_29.801410
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-26T21-34-29.801410.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-26T21-34-29.801410.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_01_26T21_34_29.801410
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-26T21-34-29.801410.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-26T21-34-29.801410.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_01_26T21_34_29.801410
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-26T21-34-29.801410.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-26T21-34-29.801410.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_01_26T21_34_29.801410
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-26T21-34-29.801410.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-26T21-34-29.801410.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_01_26T21_34_29.801410
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-26T21-34-29.801410.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-26T21-34-29.801410.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_01_26T21_34_29.801410
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-26T21-34-29.801410.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-26T21-34-29.801410.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_01_26T21_34_29.801410
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-26T21-34-29.801410.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-26T21-34-29.801410.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_01_26T21_34_29.801410
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-26T21-34-29.801410.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-26T21-34-29.801410.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_01_26T21_34_29.801410
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-26T21-34-29.801410.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-26T21-34-29.801410.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_01_26T21_34_29.801410
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-26T21-34-29.801410.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-26T21-34-29.801410.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_01_26T21_34_29.801410
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-26T21-34-29.801410.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-26T21-34-29.801410.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_01_26T21_34_29.801410
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-26T21-34-29.801410.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-26T21-34-29.801410.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_01_26T21_34_29.801410
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-26T21-34-29.801410.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-26T21-34-29.801410.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_01_26T21_34_29.801410
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-26T21-34-29.801410.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-26T21-34-29.801410.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_01_26T21_34_29.801410
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-26T21-34-29.801410.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-26T21-34-29.801410.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_01_26T21_34_29.801410
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-26T21-34-29.801410.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-26T21-34-29.801410.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_01_26T21_34_29.801410
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-26T21-34-29.801410.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-26T21-34-29.801410.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_01_26T21_34_29.801410
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-26T21-34-29.801410.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-26T21-34-29.801410.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_01_26T21_34_29.801410
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-26T21-34-29.801410.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-26T21-34-29.801410.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_01_26T21_34_29.801410
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-26T21-34-29.801410.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-26T21-34-29.801410.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_01_26T21_34_29.801410
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-26T21-34-29.801410.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-26T21-34-29.801410.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_01_26T21_34_29.801410
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-26T21-34-29.801410.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-26T21-34-29.801410.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_01_26T21_34_29.801410
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-26T21-34-29.801410.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-26T21-34-29.801410.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_01_26T21_34_29.801410
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-26T21-34-29.801410.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-26T21-34-29.801410.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_01_26T21_34_29.801410
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-26T21-34-29.801410.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-26T21-34-29.801410.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_01_26T21_34_29.801410
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-26T21-34-29.801410.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-26T21-34-29.801410.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_01_26T21_34_29.801410
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-26T21-34-29.801410.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-26T21-34-29.801410.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_01_26T21_34_29.801410
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-26T21-34-29.801410.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-26T21-34-29.801410.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_01_26T21_34_29.801410
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-26T21-34-29.801410.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-26T21-34-29.801410.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_01_26T21_34_29.801410
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-26T21-34-29.801410.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-26T21-34-29.801410.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_01_26T21_34_29.801410
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-26T21-34-29.801410.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-26T21-34-29.801410.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_01_26T21_34_29.801410
path:
- '**/details_harness|winogrande|5_2024-01-26T21-34-29.801410.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-01-26T21-34-29.801410.parquet'
- config_name: results
data_files:
- split: 2024_01_26T21_34_29.801410
path:
- results_2024-01-26T21-34-29.801410.parquet
- split: latest
path:
- results_2024-01-26T21-34-29.801410.parquet
---
# Dataset Card for Evaluation run of wang7776/Llama-2-7b-chat-hf-10-attention-sparsity
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [wang7776/Llama-2-7b-chat-hf-10-attention-sparsity](https://huggingface.co/wang7776/Llama-2-7b-chat-hf-10-attention-sparsity) 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_wang7776__Llama-2-7b-chat-hf-10-attention-sparsity",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-26T21:34:29.801410](https://huggingface.co/datasets/open-llm-leaderboard/details_wang7776__Llama-2-7b-chat-hf-10-attention-sparsity/blob/main/results_2024-01-26T21-34-29.801410.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": 0.48218747214367264,
"acc_stderr": 0.03435843254658187,
"acc_norm": 0.4868926638618298,
"acc_norm_stderr": 0.03511073988628273,
"mc1": 0.30354957160342716,
"mc1_stderr": 0.016095884155386847,
"mc2": 0.4540163852731679,
"mc2_stderr": 0.0157382073149144
},
"harness|arc:challenge|25": {
"acc": 0.4991467576791809,
"acc_stderr": 0.014611369529813276,
"acc_norm": 0.5290102389078498,
"acc_norm_stderr": 0.014586776355294321
},
"harness|hellaswag|10": {
"acc": 0.5931089424417447,
"acc_stderr": 0.004902502514738599,
"acc_norm": 0.7818163712407887,
"acc_norm_stderr": 0.0041216867002386
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.28,
"acc_stderr": 0.04512608598542129,
"acc_norm": 0.28,
"acc_norm_stderr": 0.04512608598542129
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.4148148148148148,
"acc_stderr": 0.042561937679014075,
"acc_norm": 0.4148148148148148,
"acc_norm_stderr": 0.042561937679014075
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.47368421052631576,
"acc_stderr": 0.04063302731486671,
"acc_norm": 0.47368421052631576,
"acc_norm_stderr": 0.04063302731486671
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.5,
"acc_stderr": 0.050251890762960605,
"acc_norm": 0.5,
"acc_norm_stderr": 0.050251890762960605
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.539622641509434,
"acc_stderr": 0.03067609659938918,
"acc_norm": 0.539622641509434,
"acc_norm_stderr": 0.03067609659938918
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.5069444444444444,
"acc_stderr": 0.04180806750294938,
"acc_norm": 0.5069444444444444,
"acc_norm_stderr": 0.04180806750294938
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.28,
"acc_stderr": 0.04512608598542127,
"acc_norm": 0.28,
"acc_norm_stderr": 0.04512608598542127
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.38,
"acc_stderr": 0.048783173121456316,
"acc_norm": 0.38,
"acc_norm_stderr": 0.048783173121456316
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.37,
"acc_stderr": 0.04852365870939098,
"acc_norm": 0.37,
"acc_norm_stderr": 0.04852365870939098
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.3930635838150289,
"acc_stderr": 0.03724249595817731,
"acc_norm": 0.3930635838150289,
"acc_norm_stderr": 0.03724249595817731
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.23529411764705882,
"acc_stderr": 0.04220773659171453,
"acc_norm": 0.23529411764705882,
"acc_norm_stderr": 0.04220773659171453
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.56,
"acc_stderr": 0.04988876515698589,
"acc_norm": 0.56,
"acc_norm_stderr": 0.04988876515698589
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.4127659574468085,
"acc_stderr": 0.03218471141400351,
"acc_norm": 0.4127659574468085,
"acc_norm_stderr": 0.03218471141400351
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.37719298245614036,
"acc_stderr": 0.045595221419582166,
"acc_norm": 0.37719298245614036,
"acc_norm_stderr": 0.045595221419582166
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.4689655172413793,
"acc_stderr": 0.04158632762097828,
"acc_norm": 0.4689655172413793,
"acc_norm_stderr": 0.04158632762097828
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.30158730158730157,
"acc_stderr": 0.023636975996101806,
"acc_norm": 0.30158730158730157,
"acc_norm_stderr": 0.023636975996101806
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.2619047619047619,
"acc_stderr": 0.0393253768039287,
"acc_norm": 0.2619047619047619,
"acc_norm_stderr": 0.0393253768039287
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.36,
"acc_stderr": 0.04824181513244218,
"acc_norm": 0.36,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.5193548387096775,
"acc_stderr": 0.028422687404312107,
"acc_norm": 0.5193548387096775,
"acc_norm_stderr": 0.028422687404312107
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.35467980295566504,
"acc_stderr": 0.0336612448905145,
"acc_norm": 0.35467980295566504,
"acc_norm_stderr": 0.0336612448905145
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.41,
"acc_stderr": 0.04943110704237102,
"acc_norm": 0.41,
"acc_norm_stderr": 0.04943110704237102
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.6121212121212121,
"acc_stderr": 0.038049136539710114,
"acc_norm": 0.6121212121212121,
"acc_norm_stderr": 0.038049136539710114
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.5959595959595959,
"acc_stderr": 0.03496130972056128,
"acc_norm": 0.5959595959595959,
"acc_norm_stderr": 0.03496130972056128
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.6839378238341969,
"acc_stderr": 0.033553973696861736,
"acc_norm": 0.6839378238341969,
"acc_norm_stderr": 0.033553973696861736
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.4230769230769231,
"acc_stderr": 0.025049197876042338,
"acc_norm": 0.4230769230769231,
"acc_norm_stderr": 0.025049197876042338
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.25925925925925924,
"acc_stderr": 0.026719240783712177,
"acc_norm": 0.25925925925925924,
"acc_norm_stderr": 0.026719240783712177
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.42857142857142855,
"acc_stderr": 0.032145368597886394,
"acc_norm": 0.42857142857142855,
"acc_norm_stderr": 0.032145368597886394
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.2980132450331126,
"acc_stderr": 0.037345356767871984,
"acc_norm": 0.2980132450331126,
"acc_norm_stderr": 0.037345356767871984
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.6623853211009174,
"acc_stderr": 0.02027526598663892,
"acc_norm": 0.6623853211009174,
"acc_norm_stderr": 0.02027526598663892
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.32407407407407407,
"acc_stderr": 0.03191923445686185,
"acc_norm": 0.32407407407407407,
"acc_norm_stderr": 0.03191923445686185
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.6568627450980392,
"acc_stderr": 0.03332139944668085,
"acc_norm": 0.6568627450980392,
"acc_norm_stderr": 0.03332139944668085
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.6582278481012658,
"acc_stderr": 0.03087453753755362,
"acc_norm": 0.6582278481012658,
"acc_norm_stderr": 0.03087453753755362
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.5650224215246636,
"acc_stderr": 0.033272833702713445,
"acc_norm": 0.5650224215246636,
"acc_norm_stderr": 0.033272833702713445
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.5648854961832062,
"acc_stderr": 0.04348208051644858,
"acc_norm": 0.5648854961832062,
"acc_norm_stderr": 0.04348208051644858
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.6363636363636364,
"acc_stderr": 0.043913262867240704,
"acc_norm": 0.6363636363636364,
"acc_norm_stderr": 0.043913262867240704
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.5833333333333334,
"acc_stderr": 0.04766075165356461,
"acc_norm": 0.5833333333333334,
"acc_norm_stderr": 0.04766075165356461
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.5644171779141104,
"acc_stderr": 0.03895632464138937,
"acc_norm": 0.5644171779141104,
"acc_norm_stderr": 0.03895632464138937
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.33035714285714285,
"acc_stderr": 0.04464285714285713,
"acc_norm": 0.33035714285714285,
"acc_norm_stderr": 0.04464285714285713
},
"harness|hendrycksTest-management|5": {
"acc": 0.6990291262135923,
"acc_stderr": 0.045416094465039476,
"acc_norm": 0.6990291262135923,
"acc_norm_stderr": 0.045416094465039476
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.7222222222222222,
"acc_stderr": 0.02934311479809446,
"acc_norm": 0.7222222222222222,
"acc_norm_stderr": 0.02934311479809446
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.49,
"acc_stderr": 0.05024183937956911,
"acc_norm": 0.49,
"acc_norm_stderr": 0.05024183937956911
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.6794380587484036,
"acc_stderr": 0.016688893310803768,
"acc_norm": 0.6794380587484036,
"acc_norm_stderr": 0.016688893310803768
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.5115606936416185,
"acc_stderr": 0.026911898686377927,
"acc_norm": 0.5115606936416185,
"acc_norm_stderr": 0.026911898686377927
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.23016759776536314,
"acc_stderr": 0.014078339253425812,
"acc_norm": 0.23016759776536314,
"acc_norm_stderr": 0.014078339253425812
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.5228758169934641,
"acc_stderr": 0.028599936776089782,
"acc_norm": 0.5228758169934641,
"acc_norm_stderr": 0.028599936776089782
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.5691318327974276,
"acc_stderr": 0.028125340983972714,
"acc_norm": 0.5691318327974276,
"acc_norm_stderr": 0.028125340983972714
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.5524691358024691,
"acc_stderr": 0.0276671385694227,
"acc_norm": 0.5524691358024691,
"acc_norm_stderr": 0.0276671385694227
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.37943262411347517,
"acc_stderr": 0.028947338851614105,
"acc_norm": 0.37943262411347517,
"acc_norm_stderr": 0.028947338851614105
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.3474576271186441,
"acc_stderr": 0.0121614177297498,
"acc_norm": 0.3474576271186441,
"acc_norm_stderr": 0.0121614177297498
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.45955882352941174,
"acc_stderr": 0.03027332507734576,
"acc_norm": 0.45955882352941174,
"acc_norm_stderr": 0.03027332507734576
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.4722222222222222,
"acc_stderr": 0.02019659493354119,
"acc_norm": 0.4722222222222222,
"acc_norm_stderr": 0.02019659493354119
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.5363636363636364,
"acc_stderr": 0.04776449162396197,
"acc_norm": 0.5363636363636364,
"acc_norm_stderr": 0.04776449162396197
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.5183673469387755,
"acc_stderr": 0.03198761546763127,
"acc_norm": 0.5183673469387755,
"acc_norm_stderr": 0.03198761546763127
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.6417910447761194,
"acc_stderr": 0.03390393042268815,
"acc_norm": 0.6417910447761194,
"acc_norm_stderr": 0.03390393042268815
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.69,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.69,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-virology|5": {
"acc": 0.42771084337349397,
"acc_stderr": 0.038515976837185335,
"acc_norm": 0.42771084337349397,
"acc_norm_stderr": 0.038515976837185335
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.7192982456140351,
"acc_stderr": 0.03446296217088427,
"acc_norm": 0.7192982456140351,
"acc_norm_stderr": 0.03446296217088427
},
"harness|truthfulqa:mc|0": {
"mc1": 0.30354957160342716,
"mc1_stderr": 0.016095884155386847,
"mc2": 0.4540163852731679,
"mc2_stderr": 0.0157382073149144
},
"harness|winogrande|5": {
"acc": 0.7142857142857143,
"acc_stderr": 0.012696531870038616
},
"harness|gsm8k|5": {
"acc": 0.1910538286580743,
"acc_stderr": 0.01082879119175519
}
}
```
## 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]
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## Uses
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### Direct Use
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### Out-of-Scope Use
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## Dataset Structure
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### Curation Rationale
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### Source Data
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#### Annotation process
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#### Personal and Sensitive Information
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## Bias, Risks, and Limitations
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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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CyberHarem/texas_arknights | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of texas/テキサス/德克萨斯 (Arknights)
This is the dataset of texas/テキサス/德克萨斯 (Arknights), containing 500 images and their tags.
The core tags of this character are `animal_ears, black_hair, long_hair, wolf_ears, animal_ear_fluff, hair_between_eyes, multicolored_hair, red_hair, wolf_girl, breasts, two-tone_hair, tail, wolf_tail, colored_inner_hair`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 500 | 1.05 GiB | [Download](https://huggingface.co/datasets/CyberHarem/texas_arknights/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 465.87 MiB | [Download](https://huggingface.co/datasets/CyberHarem/texas_arknights/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1315 | 1.04 GiB | [Download](https://huggingface.co/datasets/CyberHarem/texas_arknights/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 865.47 MiB | [Download](https://huggingface.co/datasets/CyberHarem/texas_arknights/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1315 | 1.69 GiB | [Download](https://huggingface.co/datasets/CyberHarem/texas_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/texas_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 | 42 |  |  |  |  |  | 1girl, long_sleeves, solo, black_capelet, white_jacket, fingerless_gloves, black_shorts, black_pantyhose, black_gloves, looking_at_viewer, short_shorts, holding_sword, brown_eyes, standing, closed_mouth, cowboy_shot, id_card, pantyhose_under_shorts, simple_background, white_background |
| 1 | 5 |  |  |  |  |  | 1girl, black_gloves, black_pantyhose, black_shorts, fingerless_gloves, food_in_mouth, long_sleeves, mouth_hold, pocky, solo, white_jacket, black_footwear, brown_eyes, looking_at_viewer, shoes, short_shorts, sitting, black_capelet, simple_background, belt, holding, id_card, knee_up, pantyhose_under_shorts, thigh_strap, white_background, yellow_eyes |
| 2 | 17 |  |  |  |  |  | 1girl, official_alternate_costume, white_jacket, black_shirt, long_sleeves, looking_at_viewer, solo, shoulder_strap, simple_background, red_gloves, upper_body, white_background, closed_mouth, id_card, fur-trimmed_sleeves, brown_eyes, open_jacket, wide_sleeves, necklace, holding, orange_eyes, blush, hand_up |
| 3 | 6 |  |  |  |  |  | 1girl, black_shirt, looking_at_viewer, necklace, official_alternate_costume, open_jacket, pocky, solo, upper_body, white_jacket, brown_eyes, food_in_mouth, long_sleeves, mouth_hold, red_gloves, shoulder_strap, fur-trimmed_jacket, holding_food, hood, orange_eyes, simple_background, white_background, blue_hair, fur-trimmed_sleeves, id_card |
| 4 | 12 |  |  |  |  |  | 1girl, black_pantyhose, black_shirt, long_sleeves, official_alternate_costume, red_gloves, solo, holding_sword, open_clothes, white_jacket, white_shorts, looking_at_viewer, shoulder_strap, short_shorts, standing, fur-trimmed_sleeves, id_card, white_background, simple_background |
| 5 | 5 |  |  |  |  |  | 1girl, black_jacket, collared_shirt, looking_at_viewer, medium_breasts, official_alternate_costume, open_jacket, ponytail, red_gloves, sidelocks, solo, upper_body, long_sleeves, red_eyes, green_necktie, holding_cigarette, simple_background, smoke, smoking, black_necktie, mouth_hold, off_shoulder, vertical-striped_clothes |
| 6 | 6 |  |  |  |  |  | 1girl, black_jacket, black_shorts, collared_shirt, long_sleeves, official_alternate_costume, ponytail, red_gloves, solo, belt, green_necktie, sidelocks, striped_clothes, thigh_strap, looking_at_viewer, open_jacket, striped_shorts, holding_sword, medium_breasts, mouth_hold, off_shoulder |
| 7 | 11 |  |  |  |  |  | 1girl, black_vest, blue_necktie, blue_shorts, collared_shirt, long_sleeves, solo, white_shirt, black_pantyhose, blue_gloves, pantyhose_under_shorts, looking_at_viewer, fingerless_gloves, holding_sword, closed_mouth, cowboy_shot, black_cape, medium_breasts, orange_eyes, yellow_eyes, boots, official_alternate_costume, very_long_hair |
| 8 | 8 |  |  |  |  |  | 1girl, black_jacket, black_pants, black_shirt, crop_top, looking_at_viewer, midriff, solo, cropped_jacket, long_sleeves, navel, black_gloves, hip_vent, open_jacket, black_footwear, fingerless_gloves, official_alternate_costume, stomach, very_long_hair, closed_mouth, simple_background, full_body, holding, medium_breasts, standing, white_background |
| 9 | 12 |  |  |  |  |  | 1girl, looking_at_viewer, solo, bare_shoulders, navel, stomach, outdoors, thighs, bare_arms, blush, choker, front-tie_bikini_top, large_breasts, medium_breasts, cleavage, red_bikini, alternate_costume, collarbone, cowboy_shot, day, ponytail, underboob, water, belt, blue_sky, closed_mouth, cloud, extra_ears, halterneck, holding, ocean, yellow_eyes |
| 10 | 9 |  |  |  |  |  | 1girl, alternate_costume, looking_at_viewer, solo, bare_shoulders, closed_mouth, thighs, yellow_eyes, black_dress, extra_ears, nail_polish, armlet, blush, cleavage, large_breasts, medium_breasts, sideboob, sleeveless_dress, bare_arms, bare_legs, very_long_hair, backless_dress, earrings, halter_dress, indoors, piercing, red_nails, sitting, thigh_strap |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | long_sleeves | solo | black_capelet | white_jacket | fingerless_gloves | black_shorts | black_pantyhose | black_gloves | looking_at_viewer | short_shorts | holding_sword | brown_eyes | standing | closed_mouth | cowboy_shot | id_card | pantyhose_under_shorts | simple_background | white_background | food_in_mouth | mouth_hold | pocky | black_footwear | shoes | sitting | belt | holding | knee_up | thigh_strap | yellow_eyes | official_alternate_costume | black_shirt | shoulder_strap | red_gloves | upper_body | fur-trimmed_sleeves | open_jacket | wide_sleeves | necklace | orange_eyes | blush | hand_up | fur-trimmed_jacket | holding_food | hood | blue_hair | open_clothes | white_shorts | black_jacket | collared_shirt | medium_breasts | ponytail | sidelocks | red_eyes | green_necktie | holding_cigarette | smoke | smoking | black_necktie | off_shoulder | vertical-striped_clothes | striped_clothes | striped_shorts | black_vest | blue_necktie | blue_shorts | white_shirt | blue_gloves | black_cape | boots | very_long_hair | black_pants | crop_top | midriff | cropped_jacket | navel | hip_vent | stomach | full_body | bare_shoulders | outdoors | thighs | bare_arms | choker | front-tie_bikini_top | large_breasts | cleavage | red_bikini | alternate_costume | collarbone | day | underboob | water | blue_sky | cloud | extra_ears | halterneck | ocean | black_dress | nail_polish | armlet | sideboob | sleeveless_dress | bare_legs | backless_dress | earrings | halter_dress | indoors | piercing | red_nails |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:---------------|:-------|:----------------|:---------------|:--------------------|:---------------|:------------------|:---------------|:--------------------|:---------------|:----------------|:-------------|:-----------|:---------------|:--------------|:----------|:-------------------------|:--------------------|:-------------------|:----------------|:-------------|:--------|:-----------------|:--------|:----------|:-------|:----------|:----------|:--------------|:--------------|:-----------------------------|:--------------|:-----------------|:-------------|:-------------|:----------------------|:--------------|:---------------|:-----------|:--------------|:--------|:----------|:---------------------|:---------------|:-------|:------------|:---------------|:---------------|:---------------|:-----------------|:-----------------|:-----------|:------------|:-----------|:----------------|:--------------------|:--------|:----------|:----------------|:---------------|:---------------------------|:------------------|:-----------------|:-------------|:---------------|:--------------|:--------------|:--------------|:-------------|:--------|:-----------------|:--------------|:-----------|:----------|:-----------------|:--------|:-----------|:----------|:------------|:-----------------|:-----------|:---------|:------------|:---------|:-----------------------|:----------------|:-----------|:-------------|:--------------------|:-------------|:------|:------------|:--------|:-----------|:--------|:-------------|:-------------|:--------|:--------------|:--------------|:---------|:-----------|:-------------------|:------------|:-----------------|:-----------|:---------------|:----------|:-----------|:------------|
| 0 | 42 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 5 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | | X | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 17 |  |  |  |  |  | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 12 |  |  |  |  |  | X | X | X | | X | | | X | | X | X | X | | X | | | X | | X | X | | | | | | | | | | | | X | X | X | X | | X | | | | | | | | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 5 |  |  |  |  |  | X | X | X | | | | | | | X | | | | | | | | | X | | | X | | | | | | | | | | X | | | X | X | | X | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 6 |  |  |  |  |  | X | X | X | | | | X | | | X | | X | | | | | | | | | | X | | | | | X | | | X | | X | | | X | | | X | | | | | | | | | | | | X | X | X | X | X | | X | | | | | X | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 11 |  |  |  |  |  | X | X | X | | | X | | X | | X | | X | | | X | X | | X | | | | | | | | | | | | | X | X | | | | | | | | | X | | | | | | | | | | X | X | | | | | | | | | | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 8 | 8 |  |  |  |  |  | X | X | X | | | X | | | X | X | | | | X | X | | | | X | X | | | | X | | | | X | | | | X | X | | | | | X | | | | | | | | | | | | X | | X | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 9 | 12 |  |  |  |  |  | X | | X | | | | | | | X | | | | | X | X | | | | | | | | | | | X | X | | | X | | | | | | | | | | | X | | | | | | | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | X | | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | |
| 10 | 9 |  |  |  |  |  | X | | X | | | | | | | X | | | | | X | | | | | | | | | | | X | | | | X | X | | | | | | | | | | | X | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | X | | X | X | | | X | X | | X | | | | | | | X | | | X | X | X | X | X | X | X | X | X | X | X | X |
|
mingyy/chinese_landscape_paintings | ---
dataset_info:
features:
- name: target
dtype: image
- name: filename
dtype: string
- name: image_caption
dtype: string
- name: hed
dtype: image
- name: source
dtype: image
splits:
- name: train
num_bytes: 44114965534.5
num_examples: 52564
download_size: 8162381811
dataset_size: 44114965534.5
---
# Dataset Card for "chinese_landscape_paintings"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
TopicNet/Reuters | ---
language:
- ru
multilinguality:
- monolingual
license: other
license_name: topicnet
license_link: >-
https://github.com/machine-intelligence-laboratory/TopicNet/blob/master/LICENSE.txt
configs:
- config_name: "bag-of-words"
default: true
data_files:
- split: train
path: "data/Reuters_BOW.csv.gz"
- config_name: "natural-order-of-words"
data_files:
- split: train
path: "data/Reuters_NOOW.csv.gz"
task_categories:
- text-classification
task_ids:
- topic-classification
- multi-class-classification
- multi-label-classification
tags:
- topic-modeling
- topic-modelling
- text-clustering
- multimodal-data
- multimodal-learning
- modalities
- document-representation
---
# Reuters
The Reuters Corpus contains 10,788 news documents totaling 1.3 million words. The documents have been classified into 90 topics, and grouped into two sets, called "training" and "test"; thus, the text with fileid 'test/14826' is a document drawn from the test set. This split is for training and testing algorithms that automatically detect the topic of a document, as we will see in chap-data-intensive.
* Language: English
* Number of topics: 90
* Number of articles: ~10.000
* Year: 2000
## References
* NLTK datasets: https://www.nltk.org/book/ch02.html.
* Dataset site: https://trec.nist.gov/data/reuters/reuters.html.
|
kpriyanshu256/MultiTabQA-multitable_pretraining-Salesforce-codet5-base_train-markdown-19000 | ---
dataset_info:
features:
- name: input_ids
sequence:
sequence: int32
- name: attention_mask
sequence:
sequence: int8
- name: labels
sequence:
sequence: int64
splits:
- name: train
num_bytes: 13336000
num_examples: 1000
download_size: 1054052
dataset_size: 13336000
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
result-kand2-sdxl-wuerst-karlo/b8542650 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 179
num_examples: 10
download_size: 1367
dataset_size: 179
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "b8542650"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
michaelwzhu/ShenNong_TCM_Dataset | ---
license: apache-2.0
---
|
arieg/bw_spec_cls_4_03_noise_200 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': '207'
'1': '210'
'2': '211'
'3': '212'
splits:
- name: train
num_bytes: 47704924.0
num_examples: 800
- name: test
num_bytes: 1192825.0
num_examples: 20
download_size: 23920920
dataset_size: 48897749.0
---
# Dataset Card for "bw_spec_cls_4_03_noise_200"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
GRPUI/autotrain-data-sgugit-model-v4 | ---
task_categories:
- text-classification
---
# AutoTrain Dataset for project: sgugit-model-v4
## Dataset Description
This dataset has been automatically processed by AutoTrain for project sgugit-model-v4.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"target": 40,
"text": "\u0433\u0434\u0435 \u043c\u043e\u0436\u043d\u043e \u043d\u0430\u0439\u0442\u0438 \u043e\u0431\u0440\u0430\u0437\u0435\u0446 \u0432\u0441\u0442\u0443\u043f\u0438\u0442\u0435\u043b\u044c\u043d\u044b\u0439 \u0438\u0441\u043f\u044b\u0442\u0430\u043d\u0438\u0435 \u043f\u0440\u043e\u0432\u0435\u0441\u0442\u0438 \u043f\u0440\u043e\u0448\u043b\u044b\u0439 \u0433\u043e\u0434 \u0447\u0442\u043e\u0431\u044b \u0431\u044b\u0442\u044c \u0433\u043e\u0442\u043e\u0432\u044b\u0439 \u043a \u043e\u043d\u0438 \u0438 \u0434\u043e\u0441\u0442\u0438\u0433\u043d\u0443\u0442\u044c \u0443\u0441\u043f\u0435\u0445"
},
{
"target": 28,
"text": "\u043a\u0430\u043a\u043e\u0439 \u0448\u0430\u0433 \u043d\u0435\u043e\u0431\u0445\u043e\u0434\u0438\u043c\u043e \u043f\u0440\u0435\u0434\u043f\u0440\u0438\u043d\u044f\u0442\u044c \u0447\u0442\u043e\u0431\u044b \u043e\u0441\u0443\u0449\u0435\u0441\u0442\u0432\u0438\u0442\u044c \u0441\u043c\u0435\u043d\u0430 \u0441\u0442\u0430\u0442\u0443\u0441 \u0441 \u043f\u043b\u0430\u0442\u043d\u044b\u0439 \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u0435 \u043d\u0430 \u0431\u044e\u0434\u0436\u0435\u0442\u043d\u044b\u0439"
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"target": "ClassLabel(names=['\u0410\u043a\u0430\u0434\u0435\u043c\u0438\u0447\u0435\u0441\u043a\u0438\u0439 \u043e\u0442\u043f\u0443\u0441\u043a', '\u0411\u044e\u0434\u0436\u0435\u0442\u043d\u044b\u0435 \u043c\u0435\u0441\u0442\u0430', '\u0412\u043e\u0441\u0441\u0442\u0430\u043d\u043e\u0432\u043b\u0435\u043d\u0438\u0435 \u043f\u043e\u0441\u043b\u0435 \u043e\u0442\u0447\u0438\u0441\u043b\u0435\u043d\u0438\u044f', '\u0413\u0440\u0430\u0444\u0438\u043a \u0437\u0430\u043d\u044f\u0442\u0438\u0439', '\u0413\u0440\u0430\u0444\u0438\u043a \u0440\u0430\u0431\u043e\u0442\u044b \u043e\u0441\u043d\u043e\u0432\u043d\u044b\u0445 \u043f\u043e\u0434\u0440\u0430\u0437\u0434\u0435\u043b\u0435\u043d\u0438\u0439 \u0421\u0413\u0423\u0413\u0438\u0422', '\u0414\u0430\u0442\u044b \u0441\u0442\u0438\u043f\u0435\u043d\u0434\u0438\u0438', '\u0414\u043e\u043a\u0443\u043c\u0435\u043d\u0442\u044b \u0434\u043b\u044f \u0437\u0430\u0441\u0435\u043b\u0435\u043d\u0438\u044f \u0432 \u043e\u0431\u0449\u0435\u0436\u0438\u0442\u0438\u0435', '\u0414\u043e\u0441\u0442\u0430\u0442\u043e\u0447\u043d\u043e \u043b\u0438 \u0415\u0413\u042d', '\u0418\u0437\u043c\u0435\u043d\u0435\u043d\u0438\u0435 \u0440\u0430\u0441\u043f\u0438\u0441\u0430\u043d\u0438\u044f \u0441\u0442\u0443\u0434\u0435\u043d\u0442\u0430\u043c\u0438', '\u0418\u043d\u0441\u0442\u0438\u0442\u0443\u0442\u044b \u0421\u0413\u0423\u0413\u0438\u0422', '\u0418\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u044f \u043e \u043f\u0440\u0435\u043f\u043e\u0434\u0430\u0432\u0430\u0442\u0435\u043b\u044f\u0445', '\u041a\u0430\u043a \u0432\u043e\u0441\u0441\u0442\u0430\u043d\u043e\u0432\u0438\u0442\u044c \u0434\u043e\u0441\u0442\u0443\u043f \u043a \u042d\u0418\u041e\u0421?', '\u041a\u0430\u043a \u043d\u0430\u0439\u0442\u0438 \u0430\u0443\u0434\u0438\u0442\u043e\u0440\u0438\u044e', '\u041a\u0430\u043a \u043d\u0430\u0439\u0442\u0438 \u0434\u0435\u043a\u0430\u043d\u0430\u0442', '\u041a\u0430\u043a \u043e\u0442\u0447\u0438\u0441\u043b\u0438\u0442\u044c\u0441\u044f?', '\u041a\u043e\u043b\u0438\u0447\u0435\u0441\u0442\u0432\u043e \u043f\u0435\u0440\u0435\u0441\u0434\u0430\u0447', '\u041a\u043e\u043b\u0438\u0447\u0435\u0441\u0442\u0432\u043e \u043f\u0440\u0435\u0434\u043c\u0435\u0442\u043e\u0432, \u0432\u043e\u0437\u043c\u043e\u0436\u043d\u044b\u0445 \u0434\u043b\u044f \u043f\u0435\u0440\u0435\u0441\u0434\u0430\u0447\u0438', '\u041c\u0430\u0442\u0435\u0440\u0438\u0430\u043b\u044c\u043d\u0430\u044f \u043f\u043e\u043c\u043e\u0449\u044c', '\u041c\u0430\u0442\u0435\u0440\u0438\u0430\u043b\u044c\u043d\u0430\u044f \u043f\u043e\u043c\u043e\u0449\u044c \u0434\u043b\u044f \u0438\u043d\u043e\u0441\u0442\u0440\u0430\u043d\u0446\u0435\u0432', '\u041d\u0430\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u044f \u0434\u043b\u044f \u043c\u0430\u0433\u0438\u0441\u0442\u0440\u0430\u0442\u0443\u0440\u044b', '\u041e\u0431\u043d\u043e\u0432\u043b\u0435\u043d\u0438\u0435 \u043e\u0446\u0435\u043d\u043e\u043a \u0432 \u0437\u0430\u0447\u0451\u0442\u043a\u0435', '\u041e\u0431\u0449\u0435\u0436\u0438\u0442\u0438\u0435 \u0434\u043b\u044f \u0438\u043d\u043e\u0433\u043e\u0440\u043e\u0434\u043d\u0438\u0445 \u0441\u0442\u0443\u0434\u0435\u043d\u0442\u043e\u0432', '\u041e\u043d\u043b\u0430\u0439\u043d \u0441\u0434\u0430\u0447\u0430 \u0434\u043e\u043a\u0443\u043c\u0435\u043d\u0442\u043e\u0432 \u0438 \u044d\u043a\u0437\u0430\u043c\u0435\u043d\u043e\u0432', '\u041e\u0442\u0441\u0440\u043e\u0447\u043a\u0430 \u043c\u0430\u0433\u0438\u0441\u0442\u0440\u0430\u043d\u0442\u0430\u043c', '\u041e\u0442\u0447\u0438\u0441\u043b\u0435\u043d\u0438\u0435 \u043f\u043e \u0437\u0430\u0434\u043e\u043b\u0436\u0435\u043d\u043d\u043e\u0441\u0442\u0438', '\u041f\u0435\u0440\u0435\u0432\u043e\u0434 \u043c\u0435\u0436\u0434\u0443 \u0433\u0440\u0443\u043f\u043f\u0430\u043c\u0438 \u043a\u0443\u0440\u0441\u0430', '\u041f\u0435\u0440\u0435\u0432\u043e\u0434 \u043d\u0430 \u0434\u0440\u0443\u0433\u043e\u0435 \u043d\u0430\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u0435', '\u041f\u0435\u0440\u0435\u0445\u043e\u0434 \u0438\u0437 \u0434\u0440\u0443\u0433\u043e\u0433\u043e \u0412\u0423\u0417\u0430', '\u041f\u0435\u0440\u0435\u0445\u043e\u0434 \u043d\u0430 \u0431\u044e\u0434\u0436\u0435\u0442', '\u041f\u043e\u043b\u0443\u0447\u0435\u043d\u0438\u0435 \u0441\u043f\u0440\u0430\u0432\u043a\u0438 \u043e\u0431 \u0443\u0447\u0451\u0431\u0435', '\u041f\u043e\u043b\u0443\u0447\u0435\u043d\u0438\u0435 \u0447\u0438\u0442\u0430\u0442\u0435\u043b\u044c\u0441\u043a\u043e\u0433\u043e \u0431\u0438\u043b\u0435\u0442\u0430', '\u041f\u043e\u0441\u043b\u0435\u0434\u043e\u0432\u0430\u0442\u0435\u043b\u044c\u043d\u043e\u0441\u0442\u044c \u0437\u0430\u0441\u0435\u043b\u0435\u043d\u0438\u044f \u0432 \u043e\u0431\u0449\u0435\u0436\u0438\u0442\u0438\u0435', '\u041f\u043e\u0442\u0435\u0440\u044f \u043f\u0440\u043e\u043f\u0443\u0441\u043a\u0430 \u0432 \u043e\u0431\u0449\u0435\u0436\u0438\u0442\u0438\u0435', '\u041f\u043e\u0442\u0435\u0440\u044f \u0441\u0442\u0443\u0434\u0435\u043d\u0447\u0435\u0441\u043a\u043e\u0433\u043e \u0431\u0438\u043b\u0435\u0442\u0430', '\u041f\u043e\u0442\u0435\u0440\u044f \u0447\u0438\u0442\u0430\u0442\u0435\u043b\u044c\u0441\u043a\u043e\u0433\u043e \u0431\u0438\u043b\u0435\u0442\u0430', '\u041f\u0440\u0430\u0432\u0438\u043b\u0430 \u043f\u0440\u043e\u0436\u0438\u0432\u0430\u043d\u0438\u044f \u0432 \u043e\u0431\u0449\u0435\u0436\u0438\u0442\u0438\u0438', '\u041f\u0440\u043e\u0434\u043e\u043b\u0436\u0438\u0442\u0435\u043b\u044c\u043d\u043e\u0441\u0442\u044c \u043f\u0440\u0430\u043a\u0442\u0438\u043a\u0438', '\u041f\u0440\u043e\u043f\u0443\u0441\u043a\u043d\u0430\u044f \u0441\u0438\u0441\u0442\u0435\u043c\u0430 \u043e\u0431\u0449\u0435\u0436\u0438\u0442\u0438\u044f', '\u041f\u0440\u043e\u0445\u043e\u0434\u043d\u044b\u0435 \u0431\u0430\u043b\u043b\u044b \u0438 \u0437\u0430\u0447\u0438\u0441\u043b\u0435\u043d\u0438\u0435', '\u041f\u0440\u043e\u0445\u043e\u0436\u0434\u0435\u043d\u0438\u0435 \u043f\u0440\u0430\u043a\u0442\u0438\u043a\u0438', '\u041f\u0440\u043e\u0448\u043b\u043e\u0433\u043e\u0434\u043d\u0438\u0435 \u044d\u043a\u0437\u0430\u043c\u0435\u043d\u044b', '\u0420\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u044b \u044d\u043a\u0437\u0430\u043c\u0435\u043d\u043e\u0432 \u0438\u0437 \u0434\u0440\u0443\u0433\u043e\u0433\u043e \u0443\u043d\u0438\u0432\u0435\u0440\u0441\u0438\u0442\u0435\u0442\u0430', '\u0421\u043e\u0441\u0442\u0430\u0432 \u043f\u0440\u0430\u043a\u0442\u0438\u043a\u0438', '\u0421\u0440\u043e\u043a\u0438 \u043f\u0440\u0438\u0451\u043c\u043d\u043e\u0439 \u043a\u0430\u043c\u043f\u0430\u043d\u0438\u0438', '\u0421\u0442\u0438\u043f\u0435\u043d\u0434\u0438\u0430\u043b\u044c\u043d\u0430\u044f \u043a\u0430\u0440\u0442\u0430', '\u0421\u0442\u0438\u043f\u0435\u043d\u0434\u0438\u044f', '\u0421\u0442\u0438\u043f\u0435\u043d\u0434\u0438\u044f \u0432 \u043b\u0435\u0442\u043d\u0438\u0439 \u043f\u0435\u0440\u0438\u043e\u0434', '\u0421\u0442\u0438\u043f\u0435\u043d\u0434\u0438\u044f \u0434\u043b\u044f \u0443\u0447\u0430\u0449\u0438\u0445\u0441\u044f \u043d\u0430 \u043f\u043b\u0430\u0442\u043d\u043e\u0439 \u043e\u0441\u043d\u043e\u0432\u0435', '\u0421\u0442\u043e\u0438\u043c\u043e\u0441\u0442\u044c \u043f\u0440\u043e\u0436\u0438\u0432\u0430\u043d\u0438\u044f \u0432 \u043e\u0431\u0449\u0435\u0436\u0438\u0442\u0438\u0438', '\u0421\u0443\u043c\u043c\u0430 \u0441\u0442\u0438\u043f\u0435\u043d\u0434\u0438\u0438', '\u0422\u0440\u0430\u043d\u0441\u043f\u043e\u0440\u0442\u043d\u0430\u044f \u043a\u0430\u0440\u0442\u0430', '\u0422\u0440\u0435\u0431\u043e\u0432\u0430\u043d\u0438\u044f \u043a \u043a\u0440\u0430\u0441\u043d\u043e\u043c\u0443 \u0434\u0438\u043f\u043b\u043e\u043c\u0443', '\u0423\u0441\u043b\u043e\u0432\u0438\u044f \u043e\u0442\u0447\u0438\u0441\u043b\u0435\u043d\u0438\u044f', '\u0423\u0442\u0435\u0440\u044f \u0434\u0438\u043f\u043b\u043e\u043c\u0430', '\u0423\u0447\u0435\u0431\u043d\u044b\u0439 \u043f\u043b\u0430\u043d', '\u0423\u0447\u0435\u0431\u043d\u044b\u0439 \u043f\u0440\u043e\u0446\u0435\u0441\u0441', '\u0427\u0442\u043e \u0442\u0430\u043a\u043e\u0435 \u042d\u0418\u041e\u0421?', '\u042d\u043b\u0435\u043a\u0442\u0440\u043e\u043d\u043d\u044b\u0435 \u0438\u0441\u0442\u043e\u0447\u043d\u0438\u043a\u0438 \u0433\u0430\u0437\u0435\u0442 \u0438 \u0436\u0443\u0440\u043d\u0430\u043b\u043e\u0432'], id=None)",
"text": "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 | 3993 |
| valid | 1006 |
|
stoddur/rmh | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 15286850153
num_examples: 5643208
download_size: 9354218561
dataset_size: 15286850153
---
# Dataset Card for "rmh"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
JihyukKim/eli5_subquestion | ---
task_categories:
- text-generation
language:
- en
size_categories:
- 1K<n<10K
dataset_info:
features:
- name: sample_id
dtype: string
- name: question
dtype: string
- name: gold_claims
sequence: string
- name: search_session_samples
sequence:
- name: turn_quality
sequence:
- name: query
dtype: string
- name: answer
dtype: string
- name: claims_nli
dtype: float32
- name: citation_recall
dtype: float32
- name: citation_precision
dtype: float32
- name: success_claims
sequence: string
- name: success_cite_sents
sequence: string
- name: fail_cite_sents
sequence: string
- name: overall_quality
struct:
- name: claims_nli
dtype: float32
- name: citation_recall
dtype: float32
- name: citation_precision
dtype: float32
splits:
- name: train
num_bytes: 879178667
num_examples: 47189
- name: test
num_bytes: 18749419
num_examples: 1000
- name: train_small
num_bytes: 9489899
num_examples: 512
- name: test_small
num_bytes: 2421264
num_examples: 128
download_size: 356075671
dataset_size: 909839249
---
|
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_230 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 1009285320.0
num_examples: 198210
download_size: 1029044525
dataset_size: 1009285320.0
---
# Dataset Card for "chunk_230"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
joey234/mmlu-college_medicine-dev | ---
dataset_info:
features:
- name: question
dtype: string
- name: choices
sequence: string
- name: answer
dtype:
class_label:
names:
'0': A
'1': B
'2': C
'3': D
- name: negate_openai_prompt
struct:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: dev
num_bytes: 3155
num_examples: 5
download_size: 0
dataset_size: 3155
---
# Dataset Card for "mmlu-college_medicine-dev"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Mahmutceliq/ses | ---
license: other
---
|
liuyanchen1015/MULTI_VALUE_stsb_completive_have_done | ---
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: score
dtype: float64
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
- name: dev
num_bytes: 29366
num_examples: 124
- name: test
num_bytes: 26890
num_examples: 105
- name: train
num_bytes: 108660
num_examples: 426
download_size: 114847
dataset_size: 164916
---
# Dataset Card for "MULTI_VALUE_stsb_completive_have_done"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
HannahRoseKirk/HatemojiCheck | ---
annotations_creators:
- expert
language_creators:
- expert-generated
languages:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: HatemojiCheck
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- hate-speech-detection
extra_gated_prompt: "We have deactivated the automatic preview for this dataset because it contains hate speech. If you want to see the preview, you can continue."
---
# Dataset Card for HatemojiCheck
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Content Warning
This datasets contains examples of hateful language.
## Dataset Description and Details
- **Repository:** https://github.com/HannahKirk/Hatemoji
- **Paper:** https://arxiv.org/abs/2108.05921
- **Point of Contact:** hannah.kirk@oii.ox.ac.uk
### Dataset Summary
HatemojiCheck is a test suite of 3,930 test cases covering seven functionalities of emoji-based hate and six identities.
HatemojiCheck contains the text for each test case and its gold-standard label from majority agreement of three annotators. We provide labels by target of hate.
HatemojiCheck can be used to evaluate the robustness of hate speech classifiers to constructions of emoji-based hate.
### Supported Tasks
Hate Speech Detection
### Languages
English
## Dataset Structure
### Data Instances
3,930 test cases
### Data Fields
case_id: The unique ID of the test case (assigned to each of the 3,930 cases generated)
templ_id: The unique ID of the template (original=.0, identity perturbation=.1, polarity perturbation=.2, emoji perturbation = .3) from which the test case was generated
test_grp_id: The ID of the set of templates (original, identity perturbation, polarity perturbation, no emoji perturbation) from which the test case was generated.
text: The text of the test case.
target: Where applicable, the protected group targeted or referenced by the test case. We cover six protected groups in the test suite: women, trans people, gay people, black people, disabled people and Muslims.
functionality: The shorthand for the functionality tested by the test case.
set: Whether the test case is an original statement, a identity perturbation, a polarity perturbation or a no emoji perturbation.
label_gold: The gold standard label (hateful/non-hateful) of the test case. All test cases within a given functionality have the same gold standard label.
unrealistic_flags: The number of annotators (/3) who flagged the test case as unrealistic.
included_in_test_suite: Indicator for whether test case is included in final HatemojiCheck test suite. All 3,930 test cases are included.
### Data Splits
All of HatemojiCheck is designated for testing models so only test is provided.
## Dataset Creation
### Curation Rationale
The purpose of HatemojiCheck is to evaluate the performance of black-box models against varied constructions of emoji-based hate. To construct HatemojiCheck, we hand-crafted 3,930 short form English-language texts using a template-based method for group identities and slurs. Each test case exemplifies one functionality and is associated with a binary gold standard label _hateful_ versus _not hateful_. All 3,930 cases were labeled by a trained team of three annotators, who could also flag examples that were unrealistic. Any test cases with multiple disagreements or flags were replaced with alternative templates and re-issued for annotation to improve the quality of examples in the final set of test cases.
### Source Data
#### Initial Data Collection and Normalization
Based on the literature, we define a list of potentially hateful emoji and words, and use Twitter's Streaming API to search for the Cartesian products of emoji--emoji and emoji--word pairs over a two week period. To identify different forms of emoji-based hate, we apply a grounded theory approach on a sample of 3,295 tweets, splitting out distinctive categories, and recursively selecting sub-categories until all key parts of the data are captured and the framework is `saturated'.
#### Who are the source language producers?
All test cases were hand-crafted by the lead author, who is a native English-speaking researcher at a UK university with extensive subject matter expertise in online harms. The test cases are in English. This choice was motivated by the researchers' and annotators' expertise, and to maximize HatemojiCheck's applicability to previous hate speech detection studies, which are predominantly conducted on English-language data. We discuss the limitations of restricting HatemojiCheck to one language and suggest that future work should prioritize expanding the test suite to other languages.
### Annotations
#### Annotation process
To validate the gold-standard labels assigned to each test case, we recruited three annotators with prior experience on hate speech projects. Annotators were given extensive guidelines, test tasks and training sessions, which included examining real-world examples of emoji-based hate from Twitter. We followed guidance for protecting annotator well-being. There were two iterative rounds of annotation. In the first round, each annotator labeled all 3,930 test cases as hateful or non-hateful, and had the option to flag unrealistic entries. Test cases with any disagreement or unrealistic flags were reviewed by the study authors (n=289). One-on-one interviews were conducted with annotators to identify dataset issues versus annotator error. From 289 test cases, 119 were identified as ambiguous or unrealistic, replaced with alternatives and re-issued to annotators for labeling. No further issues were raised. We measured inter-annotator agreement using Randolph's Kappa, obtaining a value of 0.85 for the final set of test cases, which indicates "almost perfect agreement".
#### Who are the annotators?
We recruited a team of three annotators who worked for two weeks in May 2021 and were paid £16. All annotators were female and between 30--39 years old. One had an undergraduate degree, one a taught graduate degree and one a post-graduate research degree. There were three nationalities: Argentinian, British and Iraqi, two ethnicities: White and Arab, and three religious affiliations: Catholic, Muslim and None. One annotator was a native English speaker and the others were non-native but fluent. All annotators used emoji and social media more than once per day. All annotators had seen others targeted by abuse online, and one had been targeted personally.
### Personal and Sensitive Information
HatemojiCheck contains synthetic statements so has no personal information. It does however contains harmful examples of emoji-based hate which could be disturbing or damaging to view.
## Considerations for Using the Data
### Social Impact of Dataset
HatemojiCheck contains challenging emoji examples on which commercial solutions and state-of-the-art transformer models have been proven to fail. Malicious actors could take inspiration for bypassing current detection systems on internet platforms, or in principal train a generative hate speech model. However, it also helps to evaluate model's weaknesses to emoji-based hate, so can be used to mitigate the harm to victims before a model is deployed.
### Discussion of Biases
HatemojiCheck only contains test cases against 6 identities: woman, trans people, gay people, disabled people, Black people and Muslims. It thus is biased towards evaluating hate directed at these targets. Additionally, HatemojiCheck was motivated by an empirical study of English-language tweets. The usage of emoji varies significantly across culture, country and demographic so there may be biases towards Western, English-language use of emoji.
### Other Known Limitations
While inspired by real-world instances of emoji-based hate, HatemojiCheck contains synthetic, hand-crafted test cases. These test cases are designed to be a "minimum performance standard" against which to hold models accountable. However, because the test cases are designed to have one "clear, gold-standard label" they may be easier to predict than more nuanced, complex and real-world instances of emoji-based hate.
## Additional Information
### Dataset Curators
The dataset was created by the lead author (Hannah Rose Kirk), then validated by the other authors and three annotators.
### Licensing Information
Creative Commons Attribution 4.0 International Public License. For full detail see: https://github.com/HannahKirk/Hatemoji/blob/main/LICENSE
### Citation Information
If you use this dataset, please cite our paper: Kirk, H. R., Vidgen, B., Röttger, P., Thrush, T., & Hale, S. A. (2021). Hatemoji: A test suite and adversarially-generated dataset for benchmarking and detecting emoji-based hate. arXiv preprint arXiv:2108.05921.
```
@article{kirk2021hatemoji,
title={Hatemoji: A test suite and adversarially-generated dataset for benchmarking and detecting emoji-based hate},
author={Kirk, Hannah Rose and Vidgen, Bertram and R{\"o}ttger, Paul and Thrush, Tristan and Hale, Scott A},
journal={arXiv preprint arXiv:2108.05921},
year={2021}
}
```
### Contributions
Thanks to [@HannahKirk](https://github.com/HannahKirk) for adding this dataset.
|
5cp/tmp_imdb_ft | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': neg
'1': pos
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 21120329
num_examples: 782
- name: test
num_bytes: 23158476
num_examples: 858
download_size: 736300
dataset_size: 44278805
---
# Dataset Card for "tmp_imdb_ft"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
liuyanchen1015/MULTI_VALUE_sst2_past_for_past_participle | ---
dataset_info:
features:
- name: sentence
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: score
dtype: int64
splits:
- name: dev
num_bytes: 5793
num_examples: 40
- name: test
num_bytes: 13895
num_examples: 93
- name: train
num_bytes: 221331
num_examples: 1951
download_size: 120718
dataset_size: 241019
---
# Dataset Card for "MULTI_VALUE_sst2_past_for_past_participle"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
qanastek/MORFITT | ---
license: apache-2.0
task_categories:
- text-classification
language:
- fr
tags:
- medical
- biology
pretty_name: MORFITT
size_categories:
- 1K<n<10K
---
# MORFITT
## Data ([Zenodo](https://zenodo.org/record/7893841#.ZFLFDnZBybg)) | Publication ([HAL](https://hal.science/hal-04131591/))
[Yanis LABRAK](https://www.linkedin.com/in/yanis-labrak-8a7412145/), [Richard DUFOUR](https://cv.hal.science/richard-dufour), [Mickaël ROUVIER](https://cv.hal.science/mickael-rouvier)
[](https://colab.research.google.com/drive/115EixHBcjf-se6xQeaTwZWE1i4idTNbm?usp=sharing) or [](https://github.com/qanastek/MORFITT/blob/main/TrainTransformers.py)
We introduce MORFITT, the first multi-label corpus for the classification of specialties in the medical field, in French. MORFITT is composed of 3,624 summaries of scientific articles from PubMed, annotated in 12 specialties. The article details the corpus, the experiments and the preliminary results obtained using a classifier based on the pre-trained language model CamemBERT.
For more details, please refer to our paper:
**MORFITT: A multi-label topic classification for French Biomedical literature** ([HAL](https://hal.science/hal-04131591/))
# Key Features
## Documents distribution
| Train | Dev | Test |
|-------|-------|-------|
| 1,514 | 1,022 | 1,088 |
## Multi-label distribution
| | Train | Dev | Test | Total |
|:----------------------:|:--------------:|:--------------:|:--------------:|:--------------:|
| Vétérinaire | 320 | 250 | 254 | 824 |
| Étiologie | 317 | 202 | 222 | 741 |
| Psychologie | 255 | 175 | 179 | 609 |
| Chirurgie | 223 | 169 | 157 | 549 |
| Génétique | 207 | 139 | 159 | 505 |
| Physiologie | 217 | 125 | 148 | 490 |
| Pharmacologie | 112 | 84 | 103 | 299 |
| Microbiologie | 115 | 72 | 86 | 273 |
| Immunologie | 106 | 86 | 70 | 262 |
| Chimie | 94 | 53 | 65 | 212 |
| Virologie | 76 | 57 | 67 | 200 |
| Parasitologie | 68 | 34 | 50 | 152 |
| Total | 2,110 | 1,446 | 1,560 | 5,116 |
## Number of labels per document distribution
<p align="left">
<img src="https://github.com/qanastek/MORFITT/raw/main/images/distributions_nbr_elements_colors.png" alt="drawing" width="400"/>
</p>
## Co-occurences distribution
<p align="left">
<img src="https://github.com/qanastek/MORFITT/raw/main/images/distributions_co-references-fixed.png" alt="drawing" width="400"/>
</p>
# If you use HuggingFace Transformers
```python
from datasets import load_dataset
dataset = load_dataset("qanastek/MORFITT")
print(dataset)
```
or
```python
from datasets import load_dataset
dataset_base = load_dataset(
'csv',
data_files={
'train': f"./train.tsv",
'validation': f"./dev.tsv",
'test': f"./test.tsv",
},
delimiter="\t",
)
```
# License and Citation
The code is under [Apache-2.0 License](./LICENSE).
The MORFITT dataset is licensed under *Attribution-ShareAlike 4.0 International* ([CC BY-SA 4.0](https://creativecommons.org/licenses/by/4.0/)).
If you find this project useful in your research, please cite the following papers:
```plain
Labrak, Y., Rouvier, M., & Dufour, R. (2023). MORFITT : Un corpus multi-labels d’articles scientifiques français dans le domaine biomédical. In F. Boudin, B. Daille, R. Dufour, O. Khettari, M. Houbre, L. Jourdan, & N. Kooli (Eds.), 18e Conférence en Recherche d’Information et Applications – 16e Rencontres Jeunes Chercheurs en RI – 30e Conférence sur le Traitement Automatique des Langues Naturelles – 25e Rencontre des Étudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (pp. 66–70). ATALA. https://hal.science/hal-04131591
```
or using the bibtex:
```bibtex
@inproceedings{labrak:hal-04131591,
TITLE = {{MORFITT : Un corpus multi-labels d'articles scientifiques fran{\c c}ais dans le domaine biom{\'e}dical}},
AUTHOR = {Labrak, Yanis and Rouvier, Mickael and Dufour, Richard},
URL = {https://hal.science/hal-04131591},
BOOKTITLE = {{18e Conf{\'e}rence en Recherche d'Information et Applications -- 16e Rencontres Jeunes Chercheurs en RI -- 30e Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles -- 25e Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues}},
ADDRESS = {Paris, France},
EDITOR = {Boudin, Florian and Daille, B{\'e}atrice and Dufour, Richard and Khettari, Oumaima and Houbre, Ma{\"e}l and Jourdan, L{\'e}ane and Kooli, Nihel},
PUBLISHER = {{ATALA}},
PAGES = {66-70},
YEAR = {2023},
KEYWORDS = {Analyse de documents scientifiques ; Jeux de donn{\'e}es compos{\'e}s des textes scientifiques},
PDF = {https://hal.science/hal-04131591/file/1465546.pdf},
HAL_ID = {hal-04131591},
HAL_VERSION = {v1},
}
``` |
kfahn/labeled_images_demo | ---
dataset_info:
features:
- name: image
dtype: image
- name: annotated_image
dtype: image
- name: label
dtype: string
splits:
- name: train
num_bytes: 1484716.0
num_examples: 10
download_size: 1473331
dataset_size: 1484716.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
yongchanskii/only-text-data-various-domain | ---
dataset_info:
features:
- name: docId
dtype: string
- name: category
dtype: string
- name: domainTag
dtype: string
- name: text
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 26467274.758485764
num_examples: 84235
- name: test
num_bytes: 6616897.241514237
num_examples: 21059
download_size: 20057835
dataset_size: 33084172.0
---
# Dataset Card for "only-text-data-various-domain"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/harbin_azurlane | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of harbin/ハルビン/哈尔滨 (Azur Lane)
This is the dataset of harbin/ハルビン/哈尔滨 (Azur Lane), containing 45 images and their tags.
The core tags of this character are `breasts, long_hair, large_breasts, black_hair, yellow_eyes, bangs, ponytail, multicolored_hair, streaked_hair, very_long_hair, mole, hair_between_eyes, blonde_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 | 45 | 86.59 MiB | [Download](https://huggingface.co/datasets/CyberHarem/harbin_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 45 | 40.54 MiB | [Download](https://huggingface.co/datasets/CyberHarem/harbin_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 108 | 85.54 MiB | [Download](https://huggingface.co/datasets/CyberHarem/harbin_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 45 | 70.81 MiB | [Download](https://huggingface.co/datasets/CyberHarem/harbin_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 108 | 135.73 MiB | [Download](https://huggingface.co/datasets/CyberHarem/harbin_azurlane/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/harbin_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 11 |  |  |  |  |  | 1girl, looking_at_viewer, smile, yellow_bikini, bare_shoulders, day, outdoors, solo, gold_bikini, animal_ears, blush, skindentation, thigh_strap, thighs, ass, blue_sky, cleavage, closed_mouth, cloud, collarbone, ocean, piercing, cowboy_shot, jewelry, mole_under_eye, navel, see-through, sideboob, string_bikini, water |
| 1 | 18 |  |  |  |  |  | 1girl, looking_at_viewer, sideboob, solo, smile, thighs, black_thighhighs, dress, holding, fur_trim, white_coat, chinese_clothes, hair_over_one_eye, weapon |
| 2 | 6 |  |  |  |  |  | 1girl, chinese_clothes, cleavage, hair_ornament, red_dress, brown_hair, hair_over_one_eye, looking_at_viewer, smile, solo, closed_mouth, holding_fan, wide_sleeves, flower, long_sleeves, sitting |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | smile | yellow_bikini | bare_shoulders | day | outdoors | solo | gold_bikini | animal_ears | blush | skindentation | thigh_strap | thighs | ass | blue_sky | cleavage | closed_mouth | cloud | collarbone | ocean | piercing | cowboy_shot | jewelry | mole_under_eye | navel | see-through | sideboob | string_bikini | water | black_thighhighs | dress | holding | fur_trim | white_coat | chinese_clothes | hair_over_one_eye | weapon | hair_ornament | red_dress | brown_hair | holding_fan | wide_sleeves | flower | long_sleeves | sitting |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:--------|:----------------|:-----------------|:------|:-----------|:-------|:--------------|:--------------|:--------|:----------------|:--------------|:---------|:------|:-----------|:-----------|:---------------|:--------|:-------------|:--------|:-----------|:--------------|:----------|:-----------------|:--------|:--------------|:-----------|:----------------|:--------|:-------------------|:--------|:----------|:-----------|:-------------|:------------------|:--------------------|:---------|:----------------|:------------|:-------------|:--------------|:---------------|:---------|:---------------|:----------|
| 0 | 11 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | |
| 1 | 18 |  |  |  |  |  | X | X | X | | | | | X | | | | | | X | | | | | | | | | | | | | | X | | | X | X | X | X | X | X | X | X | | | | | | | | |
| 2 | 6 |  |  |  |  |  | X | X | X | | | | | X | | | | | | | | | X | X | | | | | | | | | | | | | | | | | | X | X | | X | X | X | X | X | X | X | X |
|
Gummybear05/EY_freq_speed | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: audio
struct:
- name: array
sequence: float64
- name: path
dtype: string
- name: sample_rate
dtype: int64
- name: text
dtype: string
- name: scriptId
dtype: int64
- name: fileNm
dtype: string
- name: recrdTime
dtype: float64
- name: recrdQuality
dtype: int64
- name: recrdDt
dtype: string
- name: scriptSetNo
dtype: string
- name: recrdEnvrn
dtype: string
- name: colctUnitCode
dtype: string
- name: cityCode
dtype: string
- name: recrdUnit
dtype: string
- name: convrsThema
dtype: string
- name: gender
dtype: string
- name: recorderId
dtype: string
- name: age
dtype: int64
splits:
- name: train
num_bytes: 4865314660
num_examples: 5400
download_size: 2492988610
dataset_size: 4865314660
---
# Dataset Card for "EY_freq_speed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
zenobiax/bsracnba | ---
license: apache-2.0
---
|
pontusnorman123/swe_set2_324_sroie | ---
dataset_info:
features:
- name: id
dtype: int64
- name: words
sequence: string
- name: bboxes
sequence:
sequence: float64
- name: ner_tags
sequence:
class_label:
names:
'0': I-COMPANY
'1': I-DATE
'2': I-ADDRESS
'3': I-TOTAL
'4': O
- name: image
dtype: image
splits:
- name: train
num_bytes: 685766567.0
num_examples: 573
- name: test
num_bytes: 53446678.0
num_examples: 50
download_size: 731662862
dataset_size: 739213245.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
CyberHarem/yaia_granbluefantasy | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of yaia/ヤイア (Granblue Fantasy)
This is the dataset of yaia/ヤイア (Granblue Fantasy), containing 191 images and their tags.
The core tags of this character are `brown_hair, short_hair, horns, hairband, breasts, brown_eyes, large_breasts, hair_ornament`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:----------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 191 | 207.10 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yaia_granbluefantasy/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 191 | 125.31 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yaia_granbluefantasy/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 466 | 278.17 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yaia_granbluefantasy/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 191 | 184.83 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yaia_granbluefantasy/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 466 | 384.90 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yaia_granbluefantasy/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/yaia_granbluefantasy',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 8 |  |  |  |  |  | 1girl, christmas, bell, blush, solo, draph, fur_trim, looking_at_viewer, mittens, open_mouth, reindeer_antlers, santa_costume, hood, skirt, smile, santa_hat, bangs, capelet, dress, fake_antlers, oppai_loli, white_background |
| 1 | 5 |  |  |  |  |  | 1girl, draph, hair_bobbles, looking_at_viewer, oppai_loli, smile, solo, teddy_bear, ;d, blush, capelet, one_eye_closed, open_mouth, skirt, white_thighhighs, belt, bangs, long_sleeves, mary_janes, pouch, simple_background, white_shirt |
| 2 | 5 |  |  |  |  |  | 1girl, blush, draph, open_mouth, oppai_loli, solo, white_thighhighs, hair_bobbles, looking_at_viewer, smile, teddy_bear, petite, white_panties, ?, cameltoe, mary_janes, pink_panties, skirt_lift |
| 3 | 9 |  |  |  |  |  | 1boy, 1girl, blush, draph, oppai_loli, solo_focus, smile, penis, open_mouth, paizuri_under_clothes, pov, cum, huge_breasts, looking_at_viewer |
| 4 | 11 |  |  |  |  |  | 1boy, 1girl, draph, hetero, nipples, oppai_loli, penis, solo_focus, blush, open_mouth, sex, vaginal, hair_bobbles, nude, thighhighs, petite, bar_censor, cum_in_pussy, lying, mosaic_censoring, navel, spread_legs, tears, teddy_bear |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | christmas | bell | blush | solo | draph | fur_trim | looking_at_viewer | mittens | open_mouth | reindeer_antlers | santa_costume | hood | skirt | smile | santa_hat | bangs | capelet | dress | fake_antlers | oppai_loli | white_background | hair_bobbles | teddy_bear | ;d | one_eye_closed | white_thighhighs | belt | long_sleeves | mary_janes | pouch | simple_background | white_shirt | petite | white_panties | ? | cameltoe | pink_panties | skirt_lift | 1boy | solo_focus | penis | paizuri_under_clothes | pov | cum | huge_breasts | hetero | nipples | sex | vaginal | nude | thighhighs | bar_censor | cum_in_pussy | lying | mosaic_censoring | navel | spread_legs | tears |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:------------|:-------|:--------|:-------|:--------|:-----------|:--------------------|:----------|:-------------|:-------------------|:----------------|:-------|:--------|:--------|:------------|:--------|:----------|:--------|:---------------|:-------------|:-------------------|:---------------|:-------------|:-----|:-----------------|:-------------------|:-------|:---------------|:-------------|:--------|:--------------------|:--------------|:---------|:----------------|:----|:-----------|:---------------|:-------------|:-------|:-------------|:--------|:------------------------|:------|:------|:---------------|:---------|:----------|:------|:----------|:-------|:-------------|:-------------|:---------------|:--------|:-------------------|:--------|:--------------|:--------|
| 0 | 8 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 5 |  |  |  |  |  | X | | | X | X | X | | X | | X | | | | X | X | | X | X | | | X | | X | X | X | X | X | 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 | | | | | | | | | | | | | | | | | | | | |
| 3 | 9 |  |  |  |  |  | X | | | X | | X | | X | | X | | | | | X | | | | | | X | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | |
| 4 | 11 |  |  |  |  |  | X | | | X | | X | | | | X | | | | | | | | | | | X | | X | X | | | | | | | | | | X | | | | | | X | X | X | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
Dampish/datatt | ---
license: cc-by-nc-4.0
---
|
ravisheel40/pyschology | ---
license: mit
---
|
awettig/Pile-HackerNews-0.5B-8K-opt | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 6120908014
num_examples: 61035
- name: test
num_bytes: 64969880
num_examples: 610
download_size: 1631912620
dataset_size: 6185877894
---
# Dataset Card for "Pile-HackerNews-0.5B-8K-opt"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
gagan3012/dolphin-retrival-DAWQS-QA-corpus | ---
dataset_info:
features:
- name: _id
dtype: string
- name: text
dtype: string
splits:
- name: corpus
num_bytes: 23
num_examples: 1
- name: queries
num_bytes: 27548
num_examples: 318
download_size: 17649
dataset_size: 27571
configs:
- config_name: default
data_files:
- split: corpus
path: data/corpus-*
- split: queries
path: data/queries-*
---
|
amueller/syntactic_transformations | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
- de
license:
- mit
multilinguality:
- 2 languages
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- syntactic-evaluation
task_ids:
- syntactic-transformations
---
# Dataset Card for syntactic_transformations
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** [Needs More Information]
- **Repository:** https://github.com/sebschu/multilingual-transformations
- **Paper:** [Coloring the Blank Slate: Pre-training Imparts a Hierarchical Inductive Bias to Sequence-to-sequence Models](https://aclanthology.org/2022.findings-acl.106/)
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Aaron Mueller](mailto:amueller@jhu.edu)
### Dataset Summary
This contains the the syntactic transformations datasets used in [Coloring the Blank Slate: Pre-training Imparts a Hierarchical Inductive Bias to Sequence-to-sequence Models](https://aclanthology.org/2022.findings-acl.106/). It consists of English and German question formation and passivization transformations. This dataset also contains zero-shot cross-lingual transfer training and evaluation data.
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
English and German.
## Dataset Structure
### Data Instances
A typical data point consists of a source sequence ("src"), a target sequence ("tgt"), and a task prefix ("prefix"). The prefix indicates whether a given sequence should be kept the same in the target (indicated by the "decl:" prefix) or transformed into a question/passive ("quest:"/"passiv:", respectively). An example follows:
{"src": "the yak has entertained the walruses that have amused the newt.",
"tgt": "has the yak entertained the walruses that have amused the newt?",
"prefix": "quest: "
}
### Data Fields
- src: the original source sequence.
- tgt: the transformed target sequence.
- prefix: indicates which transformation to perform to map from the source to target sequences.
### Data Splits
The datasets are split into training, dev, test, and gen ("generalization") sets. The training sets are for fine-tuning the model. The dev and test sets are for evaluating model abilities on in-domain transformations. The generalization sets are for evaluating the inductive biases of the model.
NOTE: for the zero-shot cross-lingual transfer datasets, the generalization sets are split into in-domain and out-of-domain syntactic structures. For in-domain transformations, use "gen_rc_o" for question formation or "gen_pp_o" for passivization. For out-of-domain transformations, use "gen_rc_s" for question formation or "gen_pp_s" for passivization.
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
[Needs More Information] |
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_29 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 865858956.0
num_examples: 170043
download_size: 883351195
dataset_size: 865858956.0
---
# Dataset Card for "chunk_29"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
joey234/mmlu-security_studies-neg-prepend-fix | ---
configs:
- config_name: default
data_files:
- split: dev
path: data/dev-*
- split: test
path: data/test-*
dataset_info:
features:
- name: question
dtype: string
- name: choices
sequence: string
- name: answer
dtype:
class_label:
names:
'0': A
'1': B
'2': C
'3': D
- name: negate_openai_prompt
struct:
- name: content
dtype: string
- name: role
dtype: string
- name: neg_question
dtype: string
- name: fewshot_context
dtype: string
- name: ori_prompt
dtype: string
splits:
- name: dev
num_bytes: 13696
num_examples: 5
- name: test
num_bytes: 1861347
num_examples: 245
download_size: 22717
dataset_size: 1875043
---
# Dataset Card for "mmlu-security_studies-neg-prepend-fix"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_chargoddard__MelangeA-70b | ---
pretty_name: Evaluation run of chargoddard/MelangeA-70b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [chargoddard/MelangeA-70b](https://huggingface.co/chargoddard/MelangeA-70b) 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_chargoddard__MelangeA-70b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-17T19:47:08.035007](https://huggingface.co/datasets/open-llm-leaderboard/details_chargoddard__MelangeA-70b/blob/main/results_2023-10-17T19-47-08.035007.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\": 0.030306208053691275,\n\
\ \"em_stderr\": 0.0017555886284412359,\n \"f1\": 0.14531145134227982,\n\
\ \"f1_stderr\": 0.0023604588930624115,\n \"acc\": 0.43608650929616505,\n\
\ \"acc_stderr\": 0.008642384177128263\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.030306208053691275,\n \"em_stderr\": 0.0017555886284412359,\n\
\ \"f1\": 0.14531145134227982,\n \"f1_stderr\": 0.0023604588930624115\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.05686125852918878,\n \
\ \"acc_stderr\": 0.006378790242099637\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8153117600631413,\n \"acc_stderr\": 0.010905978112156888\n\
\ }\n}\n```"
repo_url: https://huggingface.co/chargoddard/MelangeA-70b
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_23T13_15_46.123810
path:
- '**/details_harness|arc:challenge|25_2023-08-23T13:15:46.123810.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-23T13:15:46.123810.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_17T19_47_08.035007
path:
- '**/details_harness|drop|3_2023-10-17T19-47-08.035007.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-17T19-47-08.035007.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_17T19_47_08.035007
path:
- '**/details_harness|gsm8k|5_2023-10-17T19-47-08.035007.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-17T19-47-08.035007.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_23T13_15_46.123810
path:
- '**/details_harness|hellaswag|10_2023-08-23T13:15:46.123810.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-23T13:15:46.123810.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_23T13_15_46.123810
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-23T13:15:46.123810.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-23T13:15:46.123810.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-23T13:15:46.123810.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_23T13_15_46.123810
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-23T13:15:46.123810.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-23T13:15:46.123810.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_23T13_15_46.123810
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-23T13:15:46.123810.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-23T13:15:46.123810.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_23T13_15_46.123810
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-23T13:15:46.123810.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-23T13:15:46.123810.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_23T13_15_46.123810
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-23T13:15:46.123810.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-23T13:15:46.123810.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_23T13_15_46.123810
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-23T13:15:46.123810.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-23T13:15:46.123810.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_23T13_15_46.123810
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-23T13:15:46.123810.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-23T13:15:46.123810.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_23T13_15_46.123810
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-23T13:15:46.123810.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-23T13:15:46.123810.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_23T13_15_46.123810
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-23T13:15:46.123810.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-23T13:15:46.123810.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_23T13_15_46.123810
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-23T13:15:46.123810.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-23T13:15:46.123810.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_23T13_15_46.123810
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-23T13:15:46.123810.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-23T13:15:46.123810.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_23T13_15_46.123810
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-23T13:15:46.123810.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-23T13:15:46.123810.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_23T13_15_46.123810
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-23T13:15:46.123810.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-23T13:15:46.123810.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_23T13_15_46.123810
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-23T13:15:46.123810.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-23T13:15:46.123810.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_23T13_15_46.123810
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-23T13:15:46.123810.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-23T13:15:46.123810.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_23T13_15_46.123810
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-23T13:15:46.123810.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-23T13:15:46.123810.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_23T13_15_46.123810
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-23T13:15:46.123810.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-23T13:15:46.123810.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_23T13_15_46.123810
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-23T13:15:46.123810.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-23T13:15:46.123810.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_23T13_15_46.123810
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-23T13:15:46.123810.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-23T13:15:46.123810.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_23T13_15_46.123810
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-23T13:15:46.123810.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-23T13:15:46.123810.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_23T13_15_46.123810
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-23T13:15:46.123810.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-23T13:15:46.123810.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_23T13_15_46.123810
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-23T13:15:46.123810.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-23T13:15:46.123810.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_23T13_15_46.123810
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-23T13:15:46.123810.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-23T13:15:46.123810.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_23T13_15_46.123810
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-23T13:15:46.123810.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-23T13:15:46.123810.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_23T13_15_46.123810
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-23T13:15:46.123810.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-23T13:15:46.123810.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_23T13_15_46.123810
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-23T13:15:46.123810.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-23T13:15:46.123810.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_08_23T13_15_46.123810
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-23T13:15:46.123810.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-23T13:15:46.123810.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_23T13_15_46.123810
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-23T13:15:46.123810.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-23T13:15:46.123810.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_08_23T13_15_46.123810
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-23T13:15:46.123810.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-23T13:15:46.123810.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_23T13_15_46.123810
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-23T13:15:46.123810.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-23T13:15:46.123810.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_23T13_15_46.123810
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-23T13:15:46.123810.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-23T13:15:46.123810.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_23T13_15_46.123810
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-23T13:15:46.123810.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-23T13:15:46.123810.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_23T13_15_46.123810
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-23T13:15:46.123810.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-23T13:15:46.123810.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_23T13_15_46.123810
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-23T13:15:46.123810.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-23T13:15:46.123810.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_23T13_15_46.123810
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-23T13:15:46.123810.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-23T13:15:46.123810.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_23T13_15_46.123810
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-23T13:15:46.123810.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-23T13:15:46.123810.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_23T13_15_46.123810
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-23T13:15:46.123810.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-23T13:15:46.123810.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_23T13_15_46.123810
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-23T13:15:46.123810.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-23T13:15:46.123810.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_08_23T13_15_46.123810
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-23T13:15:46.123810.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-23T13:15:46.123810.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_08_23T13_15_46.123810
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-23T13:15:46.123810.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-23T13:15:46.123810.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_23T13_15_46.123810
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-23T13:15:46.123810.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-23T13:15:46.123810.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_23T13_15_46.123810
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-23T13:15:46.123810.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-23T13:15:46.123810.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_23T13_15_46.123810
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-23T13:15:46.123810.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-23T13:15:46.123810.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_23T13_15_46.123810
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-23T13:15:46.123810.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-23T13:15:46.123810.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_23T13_15_46.123810
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-23T13:15:46.123810.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-23T13:15:46.123810.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_23T13_15_46.123810
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-23T13:15:46.123810.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-23T13:15:46.123810.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_23T13_15_46.123810
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-23T13:15:46.123810.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-23T13:15:46.123810.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_23T13_15_46.123810
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-23T13:15:46.123810.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-23T13:15:46.123810.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_23T13_15_46.123810
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-23T13:15:46.123810.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-23T13:15:46.123810.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_23T13_15_46.123810
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-23T13:15:46.123810.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-23T13:15:46.123810.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_23T13_15_46.123810
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-23T13:15:46.123810.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-23T13:15:46.123810.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_23T13_15_46.123810
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-23T13:15:46.123810.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-23T13:15:46.123810.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_23T13_15_46.123810
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-23T13:15:46.123810.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-23T13:15:46.123810.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_23T13_15_46.123810
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-23T13:15:46.123810.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-23T13:15:46.123810.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_23T13_15_46.123810
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-23T13:15:46.123810.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-23T13:15:46.123810.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_23T13_15_46.123810
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-23T13:15:46.123810.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-23T13:15:46.123810.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_23T13_15_46.123810
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-23T13:15:46.123810.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-23T13:15:46.123810.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_23T13_15_46.123810
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-23T13:15:46.123810.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-23T13:15:46.123810.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_23T13_15_46.123810
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-23T13:15:46.123810.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-23T13:15:46.123810.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_17T19_47_08.035007
path:
- '**/details_harness|winogrande|5_2023-10-17T19-47-08.035007.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-17T19-47-08.035007.parquet'
- config_name: results
data_files:
- split: 2023_10_17T19_47_08.035007
path:
- results_2023-10-17T19-47-08.035007.parquet
- split: latest
path:
- results_2023-10-17T19-47-08.035007.parquet
---
# Dataset Card for Evaluation run of chargoddard/MelangeA-70b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/chargoddard/MelangeA-70b
- **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 [chargoddard/MelangeA-70b](https://huggingface.co/chargoddard/MelangeA-70b) 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_chargoddard__MelangeA-70b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-17T19:47:08.035007](https://huggingface.co/datasets/open-llm-leaderboard/details_chargoddard__MelangeA-70b/blob/main/results_2023-10-17T19-47-08.035007.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": 0.030306208053691275,
"em_stderr": 0.0017555886284412359,
"f1": 0.14531145134227982,
"f1_stderr": 0.0023604588930624115,
"acc": 0.43608650929616505,
"acc_stderr": 0.008642384177128263
},
"harness|drop|3": {
"em": 0.030306208053691275,
"em_stderr": 0.0017555886284412359,
"f1": 0.14531145134227982,
"f1_stderr": 0.0023604588930624115
},
"harness|gsm8k|5": {
"acc": 0.05686125852918878,
"acc_stderr": 0.006378790242099637
},
"harness|winogrande|5": {
"acc": 0.8153117600631413,
"acc_stderr": 0.010905978112156888
}
}
```
### 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] |
autoevaluate/autoeval-eval-futin__guess-vi_3-74fd83-2087367155 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- futin/guess
eval_info:
task: text_zero_shot_classification
model: bigscience/bloom-7b1
metrics: []
dataset_name: futin/guess
dataset_config: vi_3
dataset_split: test
col_mapping:
text: text
classes: classes
target: target
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: bigscience/bloom-7b1
* Dataset: futin/guess
* Config: vi_3
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@futin](https://huggingface.co/futin) for evaluating this model. |
yahanyang777/DSI_web_content | ---
license: mit
---
|
ariG23498/images | ---
license: mit
---
|
data-store/prepare-for-training | ---
dataset_info:
features:
- name: text
dtype: string
- name: labels
sequence: string
splits:
- name: train
num_bytes: 1127250.2259793815
num_examples: 6789
download_size: 574030
dataset_size: 1127250.2259793815
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
chathuranga-jayanath/context-5-predict-token-for-fine-tune-without-comments-from-finmath-0.1 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: filepath
dtype: string
- name: start_bug_line
dtype: int64
- name: end_bug_line
dtype: int64
- name: bug
dtype: string
- name: fix
dtype: string
- name: ctx
dtype: string
splits:
- name: train
num_bytes: 15408906
num_examples: 15574
- name: validation
num_bytes: 1921798
num_examples: 1946
- name: test
num_bytes: 1911003
num_examples: 1946
download_size: 6808492
dataset_size: 19241707
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
CyberHarem/rmb_93_girlsfrontline | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of rmb_93/RMB-93/RMB-93 (Girls' Frontline)
This is the dataset of rmb_93/RMB-93/RMB-93 (Girls' Frontline), containing 23 images and their tags.
The core tags of this character are `breasts, short_hair, red_eyes, white_hair, large_breasts, bangs, earrings, hair_between_eyes`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:-----------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 23 | 25.87 MiB | [Download](https://huggingface.co/datasets/CyberHarem/rmb_93_girlsfrontline/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 23 | 15.37 MiB | [Download](https://huggingface.co/datasets/CyberHarem/rmb_93_girlsfrontline/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 50 | 29.04 MiB | [Download](https://huggingface.co/datasets/CyberHarem/rmb_93_girlsfrontline/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 23 | 22.79 MiB | [Download](https://huggingface.co/datasets/CyberHarem/rmb_93_girlsfrontline/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 50 | 38.52 MiB | [Download](https://huggingface.co/datasets/CyberHarem/rmb_93_girlsfrontline/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/rmb_93_girlsfrontline',
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, bare_shoulders, looking_at_viewer, solo, jewelry, smile, white_dress, white_thighhighs, medium_breasts, choker, closed_mouth, collarbone, full_body, garter_straps, high_heels, official_alternate_costume, white_background, blush, elbow_gloves, holding_gun, shotgun, simple_background, wedding_dress, white_gloves, blue_dress, cleavage_cutout, flower, layered_dress, long_sleeves, o-ring, off-shoulder_dress, strapless_dress |
| 1 | 9 |  |  |  |  |  | 1girl, looking_at_viewer, ribbed_sweater, solo, bare_shoulders, smile, turtleneck, off_shoulder, simple_background, sleeveless, boots, gun, star_(symbol), sweater_dress, white_background, black_thighhighs, fur-trimmed_jacket, garter_straps, holding, necklace, white_thighhighs |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | bare_shoulders | looking_at_viewer | solo | jewelry | smile | white_dress | white_thighhighs | medium_breasts | choker | closed_mouth | collarbone | full_body | garter_straps | high_heels | official_alternate_costume | white_background | blush | elbow_gloves | holding_gun | shotgun | simple_background | wedding_dress | white_gloves | blue_dress | cleavage_cutout | flower | layered_dress | long_sleeves | o-ring | off-shoulder_dress | strapless_dress | ribbed_sweater | turtleneck | off_shoulder | sleeveless | boots | gun | star_(symbol) | sweater_dress | black_thighhighs | fur-trimmed_jacket | holding | necklace |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------------|:--------------------|:-------|:----------|:--------|:--------------|:-------------------|:-----------------|:---------|:---------------|:-------------|:------------|:----------------|:-------------|:-----------------------------|:-------------------|:--------|:---------------|:--------------|:----------|:--------------------|:----------------|:---------------|:-------------|:------------------|:---------|:----------------|:---------------|:---------|:---------------------|:------------------|:-----------------|:-------------|:---------------|:-------------|:--------|:------|:----------------|:----------------|:-------------------|:---------------------|:----------|:-----------|
| 0 | 10 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | |
| 1 | 9 |  |  |  |  |  | X | X | X | X | | X | | X | | | | | | X | | | X | | | | | X | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X |
|
Jayveersinh-Raj/hindi-abuse-detection-test | ---
language:
- hi
---
# Dataset details
labelled dataset in hindi language for test with ground truth available
# Labels
Binary : hatespeech: 1, Neutral: 0
|
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