datasetId stringlengths 2 117 | card stringlengths 19 1.01M |
|---|---|
VinayHajare/Fruits-30 | ---
license: apache-2.0
task_categories:
- image-classification
language:
- en
tags:
- multiclass-image-classification
- vision
size_categories:
- n<1K
---
# Fruits30 Dataset
## Description:
The Fruits30 dataset is a collection of images featuring 30 different types of fruits. Each image has been preprocessed and standardized to a size of 224x224 pixels, ensuring uniformity in the dataset.
## Dataset Composition:
- **Number of Classes:** 30
- **Image Resolution:** 224x224 pixels
- **Total Images:** 826
## Classes:
0 : acerolas
1 : apples
2 : apricots
3 : avocados
4 : bananas
5 : blackberries
6 : blueberries
7 : cantaloupes
8 : cherries
9 : coconuts
10 : figs
11 : grapefruits
12 : grapes
13 : guava
14 : kiwifruit
15 : lemons
16 : limes
17 : mangos
18 : olives
19 : oranges
20 : passionfruit
21 : peaches
22 : pears
23 : pineapples
24 : plums
25 : pomegranates
26 : raspberries
27 : strawberries
28 : tomatoes
29 : watermelons
## Preprocessing:
Images have undergone preprocessing to maintain consistency and facilitate model training. Preprocessing steps may include resizing, normalization, and other enhancements.
## Intended Use:
The Fruits30 dataset is suitable for tasks such as image classification, object recognition, and machine learning model training within the domain of fruit identification.
## Sources:
Croudsource.
## Note:
Ensure proper attribution and compliance with the dataset's licensing terms when using it for research or development purposes. |
distilled-from-one-sec-cv12/chunk_210 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 1131960108
num_examples: 220569
download_size: 1157378992
dataset_size: 1131960108
---
# Dataset Card for "chunk_210"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
anan-2024/twitter_dataset_1713127874 | ---
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: 92026
num_examples: 235
download_size: 54546
dataset_size: 92026
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
AY2324S2-CS4248-Team-47/gec-dpo-ultrafeedback | ---
dataset_info:
features:
- name: chosen
dtype: string
- name: prompt
dtype: string
- name: rejected
dtype: string
splits:
- name: train
num_bytes: 1494850
num_examples: 500
download_size: 928201
dataset_size: 1494850
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Balag4u/SCM_Gloss | ---
license: unknown
---
|
CyberHarem/croissant_arknights | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of croissant/クロワッサン/可颂 (Arknights)
This is the dataset of croissant/クロワッサン/可颂 (Arknights), containing 202 images and their tags.
The core tags of this character are `horns, orange_hair, green_eyes, long_hair, ahoge, breasts, visor_cap, cow_horns, medium_breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:---------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 202 | 266.75 MiB | [Download](https://huggingface.co/datasets/CyberHarem/croissant_arknights/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 1200 | 202 | 230.96 MiB | [Download](https://huggingface.co/datasets/CyberHarem/croissant_arknights/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 436 | 425.48 MiB | [Download](https://huggingface.co/datasets/CyberHarem/croissant_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/croissant_arknights',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 16 |  |  |  |  |  | 1girl, fur-trimmed_jacket, midriff, navel, open_jacket, white_gloves, black_jacket, black_shorts, collarbone, long_sleeves, open_mouth, solo, crop_top, short_shorts, stomach, :d, fang, looking_at_viewer, cleavage, simple_background, standing, white_background, bare_shoulders, cowboy_shot, hand_up, off_shoulder, blush, groin, hair_between_eyes, holding, thighs, belt, black_gloves, black_sports_bra, short_hair, small_breasts |
| 1 | 6 |  |  |  |  |  | 1girl, bare_shoulders, belt, black_gloves, black_shorts, crop_top, day, holding, midriff, navel, official_alternate_costume, outdoors, short_shorts, solo, stomach, thigh_strap, twin_braids, :d, goggles_around_neck, looking_at_viewer, motorcycle, open_mouth, bandages, blue_sky, boots, desert, fingerless_gloves, standing, arm_up, black_footwear, cloud, hammer, pouch, thighs |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | fur-trimmed_jacket | midriff | navel | open_jacket | white_gloves | black_jacket | black_shorts | collarbone | long_sleeves | open_mouth | solo | crop_top | short_shorts | stomach | :d | fang | looking_at_viewer | cleavage | simple_background | standing | white_background | bare_shoulders | cowboy_shot | hand_up | off_shoulder | blush | groin | hair_between_eyes | holding | thighs | belt | black_gloves | black_sports_bra | short_hair | small_breasts | day | official_alternate_costume | outdoors | thigh_strap | twin_braids | goggles_around_neck | motorcycle | bandages | blue_sky | boots | desert | fingerless_gloves | arm_up | black_footwear | cloud | hammer | pouch |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------------------|:----------|:--------|:--------------|:---------------|:---------------|:---------------|:-------------|:---------------|:-------------|:-------|:-----------|:---------------|:----------|:-----|:-------|:--------------------|:-----------|:--------------------|:-----------|:-------------------|:-----------------|:--------------|:----------|:---------------|:--------|:--------|:--------------------|:----------|:---------|:-------|:---------------|:-------------------|:-------------|:----------------|:------|:-----------------------------|:-----------|:--------------|:--------------|:----------------------|:-------------|:-----------|:-----------|:--------|:---------|:--------------------|:---------|:-----------------|:--------|:---------|:--------|
| 0 | 16 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | |
| 1 | 6 |  |  |  |  |  | X | | X | X | | | | X | | | X | X | X | X | X | X | | X | | | X | | X | | | | | | | X | X | X | X | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
jquann1998/Pepper-Ann-Pearson | ---
license: cc
---
|
Carlosgg14/vegetavoicemakers | ---
license: openrail
---
|
tonyassi/vogue-runway-top15-512px | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': alexander mcqueen,fall 1996 ready to wear
'1': alexander mcqueen,fall 1997 ready to wear
'2': alexander mcqueen,fall 1998 ready to wear
'3': alexander mcqueen,fall 1999 ready to wear
'4': alexander mcqueen,fall 2000 ready to wear
'5': alexander mcqueen,fall 2001 ready to wear
'6': alexander mcqueen,fall 2002 ready to wear
'7': alexander mcqueen,fall 2003 ready to wear
'8': alexander mcqueen,fall 2004 ready to wear
'9': alexander mcqueen,fall 2005 menswear
'10': alexander mcqueen,fall 2005 ready to wear
'11': alexander mcqueen,fall 2006 menswear
'12': alexander mcqueen,fall 2006 ready to wear
'13': alexander mcqueen,fall 2007 menswear
'14': alexander mcqueen,fall 2007 ready to wear
'15': alexander mcqueen,fall 2008 menswear
'16': alexander mcqueen,fall 2008 ready to wear
'17': alexander mcqueen,fall 2009 ready to wear
'18': alexander mcqueen,fall 2010 menswear
'19': alexander mcqueen,fall 2010 ready to wear
'20': alexander mcqueen,fall 2011 menswear
'21': alexander mcqueen,fall 2011 ready to wear
'22': alexander mcqueen,fall 2012 menswear
'23': alexander mcqueen,fall 2012 ready to wear
'24': alexander mcqueen,fall 2013 menswear
'25': alexander mcqueen,fall 2013 ready to wear
'26': alexander mcqueen,fall 2014 menswear
'27': alexander mcqueen,fall 2014 ready to wear
'28': alexander mcqueen,fall 2015 menswear
'29': alexander mcqueen,fall 2015 ready to wear
'30': alexander mcqueen,fall 2016 menswear
'31': alexander mcqueen,fall 2016 ready to wear
'32': alexander mcqueen,fall 2017 menswear
'33': alexander mcqueen,fall 2017 ready to wear
'34': alexander mcqueen,fall 2018 menswear
'35': alexander mcqueen,fall 2018 ready to wear
'36': alexander mcqueen,fall 2019 menswear
'37': alexander mcqueen,fall 2019 ready to wear
'38': alexander mcqueen,fall 2020 menswear
'39': alexander mcqueen,fall 2020 ready to wear
'40': alexander mcqueen,fall 2021 menswear
'41': alexander mcqueen,fall 2021 ready to wear
'42': alexander mcqueen,fall 2022 menswear
'43': alexander mcqueen,fall 2022 ready to wear
'44': alexander mcqueen,fall 2023 menswear
'45': alexander mcqueen,fall 2023 ready to wear
'46': alexander mcqueen,pre fall 2009
'47': alexander mcqueen,pre fall 2011
'48': alexander mcqueen,pre fall 2012
'49': alexander mcqueen,pre fall 2013
'50': alexander mcqueen,pre fall 2014
'51': alexander mcqueen,pre fall 2015
'52': alexander mcqueen,pre fall 2016
'53': alexander mcqueen,pre fall 2017
'54': alexander mcqueen,pre fall 2018
'55': alexander mcqueen,pre fall 2019
'56': alexander mcqueen,pre fall 2020
'57': alexander mcqueen,pre fall 2021
'58': alexander mcqueen,pre fall 2021 menswear
'59': alexander mcqueen,pre fall 2022
'60': alexander mcqueen,pre fall 2023
'61': alexander mcqueen,resort 2009
'62': alexander mcqueen,resort 2010
'63': alexander mcqueen,resort 2011
'64': alexander mcqueen,resort 2012
'65': alexander mcqueen,resort 2013
'66': alexander mcqueen,resort 2014
'67': alexander mcqueen,resort 2015
'68': alexander mcqueen,resort 2016
'69': alexander mcqueen,resort 2017
'70': alexander mcqueen,resort 2018
'71': alexander mcqueen,resort 2019
'72': alexander mcqueen,resort 2020
'73': alexander mcqueen,resort 2021
'74': alexander mcqueen,resort 2022
'75': alexander mcqueen,resort 2023
'76': alexander mcqueen,spring 1995 ready to wear
'77': alexander mcqueen,spring 1996 ready to wear
'78': alexander mcqueen,spring 1997 ready to wear
'79': alexander mcqueen,spring 1998 ready to wear
'80': alexander mcqueen,spring 1999 ready to wear
'81': alexander mcqueen,spring 2000 ready to wear
'82': alexander mcqueen,spring 2001 ready to wear
'83': alexander mcqueen,spring 2002 ready to wear
'84': alexander mcqueen,spring 2003 ready to wear
'85': alexander mcqueen,spring 2004 ready to wear
'86': alexander mcqueen,spring 2005 menswear
'87': alexander mcqueen,spring 2005 ready to wear
'88': alexander mcqueen,spring 2006 menswear
'89': alexander mcqueen,spring 2006 ready to wear
'90': alexander mcqueen,spring 2007 menswear
'91': alexander mcqueen,spring 2007 ready to wear
'92': alexander mcqueen,spring 2008 menswear
'93': alexander mcqueen,spring 2008 ready to wear
'94': alexander mcqueen,spring 2009 menswear
'95': alexander mcqueen,spring 2009 ready to wear
'96': alexander mcqueen,spring 2010 menswear
'97': alexander mcqueen,spring 2010 ready to wear
'98': alexander mcqueen,spring 2011 menswear
'99': alexander mcqueen,spring 2011 ready to wear
'100': alexander mcqueen,spring 2012 menswear
'101': alexander mcqueen,spring 2012 ready to wear
'102': alexander mcqueen,spring 2013 menswear
'103': alexander mcqueen,spring 2013 ready to wear
'104': alexander mcqueen,spring 2014 menswear
'105': alexander mcqueen,spring 2014 ready to wear
'106': alexander mcqueen,spring 2015 menswear
'107': alexander mcqueen,spring 2015 ready to wear
'108': alexander mcqueen,spring 2016 menswear
'109': alexander mcqueen,spring 2016 ready to wear
'110': alexander mcqueen,spring 2017 menswear
'111': alexander mcqueen,spring 2017 ready to wear
'112': alexander mcqueen,spring 2018 menswear
'113': alexander mcqueen,spring 2018 ready to wear
'114': alexander mcqueen,spring 2019 menswear
'115': alexander mcqueen,spring 2019 ready to wear
'116': alexander mcqueen,spring 2020 menswear
'117': alexander mcqueen,spring 2020 ready to wear
'118': alexander mcqueen,spring 2021 menswear
'119': alexander mcqueen,spring 2021 ready to wear
'120': alexander mcqueen,spring 2022 menswear
'121': alexander mcqueen,spring 2022 ready to wear
'122': alexander mcqueen,spring 2023 menswear
'123': alexander mcqueen,spring 2023 ready to wear
'124': alexander mcqueen,spring 2024 menswear
'125': alexander mcqueen,spring 2024 ready to wear
'126': armani prive,fall 2005 couture
'127': armani prive,fall 2006 couture
'128': armani prive,fall 2007 couture
'129': armani prive,fall 2008 couture
'130': armani prive,fall 2009 couture
'131': armani prive,fall 2010 couture
'132': armani prive,fall 2011 couture
'133': armani prive,fall 2012 couture
'134': armani prive,fall 2013 couture
'135': armani prive,fall 2014 couture
'136': armani prive,fall 2015 couture
'137': armani prive,fall 2016 couture
'138': armani prive,fall 2017 couture
'139': armani prive,fall 2018 couture
'140': armani prive,fall 2019 couture
'141': armani prive,fall 2021 couture
'142': armani prive,fall 2022 couture
'143': armani prive,fall 2023 couture
'144': armani prive,spring 2005 couture
'145': armani prive,spring 2006 couture
'146': armani prive,spring 2007 couture
'147': armani prive,spring 2008 couture
'148': armani prive,spring 2009 couture
'149': armani prive,spring 2010 couture
'150': armani prive,spring 2011 couture
'151': armani prive,spring 2012 couture
'152': armani prive,spring 2013 couture
'153': armani prive,spring 2014 couture
'154': armani prive,spring 2015 couture
'155': armani prive,spring 2016 couture
'156': armani prive,spring 2017 couture
'157': armani prive,spring 2018 couture
'158': armani prive,spring 2019 couture
'159': armani prive,spring 2020 couture
'160': armani prive,spring 2021 couture
'161': armani prive,spring 2023 couture
'162': balenciaga,fall 2000 ready to wear
'163': balenciaga,fall 2001 ready to wear
'164': balenciaga,fall 2002 ready to wear
'165': balenciaga,fall 2003 ready to wear
'166': balenciaga,fall 2004 ready to wear
'167': balenciaga,fall 2005 ready to wear
'168': balenciaga,fall 2006 ready to wear
'169': balenciaga,fall 2007 menswear
'170': balenciaga,fall 2007 ready to wear
'171': balenciaga,fall 2008 ready to wear
'172': balenciaga,fall 2009 ready to wear
'173': balenciaga,fall 2010 ready to wear
'174': balenciaga,fall 2011 menswear
'175': balenciaga,fall 2011 ready to wear
'176': balenciaga,fall 2012 menswear
'177': balenciaga,fall 2012 ready to wear
'178': balenciaga,fall 2013 menswear
'179': balenciaga,fall 2013 ready to wear
'180': balenciaga,fall 2014 menswear
'181': balenciaga,fall 2014 ready to wear
'182': balenciaga,fall 2015 menswear
'183': balenciaga,fall 2015 ready to wear
'184': balenciaga,fall 2016 ready to wear
'185': balenciaga,fall 2017 menswear
'186': balenciaga,fall 2017 ready to wear
'187': balenciaga,fall 2018 ready to wear
'188': balenciaga,fall 2019 menswear
'189': balenciaga,fall 2019 ready to wear
'190': balenciaga,fall 2020 menswear
'191': balenciaga,fall 2020 ready to wear
'192': balenciaga,fall 2021 couture
'193': balenciaga,fall 2021 menswear
'194': balenciaga,fall 2021 ready to wear
'195': balenciaga,fall 2022 couture
'196': balenciaga,fall 2022 ready to wear
'197': balenciaga,fall 2023 couture
'198': balenciaga,fall 2023 ready to wear
'199': balenciaga,pre fall 2008
'200': balenciaga,pre fall 2009
'201': balenciaga,pre fall 2010
'202': balenciaga,pre fall 2011
'203': balenciaga,pre fall 2012
'204': balenciaga,pre fall 2013
'205': balenciaga,pre fall 2014
'206': balenciaga,pre fall 2015
'207': balenciaga,pre fall 2016
'208': balenciaga,pre fall 2017
'209': balenciaga,pre fall 2018
'210': balenciaga,pre fall 2019
'211': balenciaga,pre fall 2020
'212': balenciaga,pre fall 2021
'213': balenciaga,pre fall 2022
'214': balenciaga,pre fall 2023
'215': balenciaga,pre fall 2024
'216': balenciaga,resort 2008
'217': balenciaga,resort 2009
'218': balenciaga,resort 2010
'219': balenciaga,resort 2011
'220': balenciaga,resort 2012
'221': balenciaga,resort 2013
'222': balenciaga,resort 2014
'223': balenciaga,resort 2015
'224': balenciaga,resort 2016
'225': balenciaga,resort 2017
'226': balenciaga,resort 2018
'227': balenciaga,resort 2019
'228': balenciaga,resort 2020
'229': balenciaga,resort 2021
'230': balenciaga,resort 2022
'231': balenciaga,resort 2023
'232': balenciaga,resort 2024
'233': balenciaga,spring 1998 ready to wear
'234': balenciaga,spring 2000 ready to wear
'235': balenciaga,spring 2001 ready to wear
'236': balenciaga,spring 2002 ready to wear
'237': balenciaga,spring 2003 ready to wear
'238': balenciaga,spring 2004 ready to wear
'239': balenciaga,spring 2005 ready to wear
'240': balenciaga,spring 2006 ready to wear
'241': balenciaga,spring 2007 menswear
'242': balenciaga,spring 2007 ready to wear
'243': balenciaga,spring 2008 menswear
'244': balenciaga,spring 2008 ready to wear
'245': balenciaga,spring 2009 ready to wear
'246': balenciaga,spring 2010 ready to wear
'247': balenciaga,spring 2011 menswear
'248': balenciaga,spring 2011 ready to wear
'249': balenciaga,spring 2012 menswear
'250': balenciaga,spring 2012 ready to wear
'251': balenciaga,spring 2013 menswear
'252': balenciaga,spring 2013 ready to wear
'253': balenciaga,spring 2014 menswear
'254': balenciaga,spring 2014 ready to wear
'255': balenciaga,spring 2015 menswear
'256': balenciaga,spring 2015 ready to wear
'257': balenciaga,spring 2016 menswear
'258': balenciaga,spring 2016 ready to wear
'259': balenciaga,spring 2017 menswear
'260': balenciaga,spring 2017 ready to wear
'261': balenciaga,spring 2018 menswear
'262': balenciaga,spring 2018 ready to wear
'263': balenciaga,spring 2019 ready to wear
'264': balenciaga,spring 2020 menswear
'265': balenciaga,spring 2020 ready to wear
'266': balenciaga,spring 2021 menswear
'267': balenciaga,spring 2021 ready to wear
'268': balenciaga,spring 2022 ready to wear
'269': balenciaga,spring 2023 ready to wear
'270': balenciaga,spring 2024 ready to wear
'271': calvin klein collection,fall 1995 ready to wear
'272': calvin klein collection,fall 1996 ready to wear
'273': calvin klein collection,fall 1997 ready to wear
'274': calvin klein collection,fall 1998 ready to wear
'275': calvin klein collection,fall 1999 ready to wear
'276': calvin klein collection,fall 2000 ready to wear
'277': calvin klein collection,fall 2001 ready to wear
'278': calvin klein collection,fall 2002 ready to wear
'279': calvin klein collection,fall 2003 ready to wear
'280': calvin klein collection,fall 2004 ready to wear
'281': calvin klein collection,fall 2005 menswear
'282': calvin klein collection,fall 2005 ready to wear
'283': calvin klein collection,fall 2006 menswear
'284': calvin klein collection,fall 2006 ready to wear
'285': calvin klein collection,fall 2007 menswear
'286': calvin klein collection,fall 2007 ready to wear
'287': calvin klein collection,fall 2008 menswear
'288': calvin klein collection,fall 2008 ready to wear
'289': calvin klein collection,fall 2009 ready to wear
'290': calvin klein collection,fall 2010 menswear
'291': calvin klein collection,fall 2010 ready to wear
'292': calvin klein collection,fall 2011 menswear
'293': calvin klein collection,fall 2011 ready to wear
'294': calvin klein collection,fall 2012 menswear
'295': calvin klein collection,fall 2012 ready to wear
'296': calvin klein collection,fall 2013 menswear
'297': calvin klein collection,fall 2013 ready to wear
'298': calvin klein collection,fall 2014 menswear
'299': calvin klein collection,fall 2014 ready to wear
'300': calvin klein collection,fall 2015 menswear
'301': calvin klein collection,fall 2015 ready to wear
'302': calvin klein collection,fall 2016 menswear
'303': calvin klein collection,fall 2016 ready to wear
'304': calvin klein collection,pre fall 2008
'305': calvin klein collection,pre fall 2009
'306': calvin klein collection,pre fall 2010
'307': calvin klein collection,pre fall 2011
'308': calvin klein collection,pre fall 2012
'309': calvin klein collection,pre fall 2013
'310': calvin klein collection,pre fall 2014
'311': calvin klein collection,pre fall 2015
'312': calvin klein collection,pre fall 2016
'313': calvin klein collection,resort 2008
'314': calvin klein collection,resort 2009
'315': calvin klein collection,resort 2010
'316': calvin klein collection,resort 2011
'317': calvin klein collection,resort 2012
'318': calvin klein collection,resort 2013
'319': calvin klein collection,resort 2014
'320': calvin klein collection,resort 2015
'321': calvin klein collection,resort 2016
'322': calvin klein collection,resort 2017
'323': calvin klein collection,spring 1994 ready to wear
'324': calvin klein collection,spring 1995 ready to wear
'325': calvin klein collection,spring 1996 ready to wear
'326': calvin klein collection,spring 1997 ready to wear
'327': calvin klein collection,spring 1998 ready to wear
'328': calvin klein collection,spring 1999 ready to wear
'329': calvin klein collection,spring 2000 ready to wear
'330': calvin klein collection,spring 2001 ready to wear
'331': calvin klein collection,spring 2002 ready to wear
'332': calvin klein collection,spring 2003 ready to wear
'333': calvin klein collection,spring 2004 ready to wear
'334': calvin klein collection,spring 2005 menswear
'335': calvin klein collection,spring 2005 ready to wear
'336': calvin klein collection,spring 2006 menswear
'337': calvin klein collection,spring 2006 ready to wear
'338': calvin klein collection,spring 2007 menswear
'339': calvin klein collection,spring 2007 ready to wear
'340': calvin klein collection,spring 2008 menswear
'341': calvin klein collection,spring 2008 ready to wear
'342': calvin klein collection,spring 2009 menswear
'343': calvin klein collection,spring 2009 ready to wear
'344': calvin klein collection,spring 2010 menswear
'345': calvin klein collection,spring 2010 ready to wear
'346': calvin klein collection,spring 2011 menswear
'347': calvin klein collection,spring 2011 ready to wear
'348': calvin klein collection,spring 2012 menswear
'349': calvin klein collection,spring 2012 ready to wear
'350': calvin klein collection,spring 2013 menswear
'351': calvin klein collection,spring 2013 ready to wear
'352': calvin klein collection,spring 2014 menswear
'353': calvin klein collection,spring 2014 ready to wear
'354': calvin klein collection,spring 2015 menswear
'355': calvin klein collection,spring 2015 ready to wear
'356': calvin klein collection,spring 2016 menswear
'357': calvin klein collection,spring 2016 ready to wear
'358': calvin klein collection,spring 2017 menswear
'359': calvin klein,fall 2017 menswear
'360': calvin klein,fall 2017 ready to wear
'361': calvin klein,fall 2018 menswear
'362': calvin klein,fall 2018 ready to wear
'363': calvin klein,pre fall 2019
'364': calvin klein,resort 2019
'365': calvin klein,spring 2018 menswear
'366': calvin klein,spring 2018 ready to wear
'367': calvin klein,spring 2019 menswear
'368': calvin klein,spring 2019 ready to wear
'369': chanel,fall 1991 ready to wear
'370': chanel,fall 1994 ready to wear
'371': chanel,fall 1995 couture
'372': chanel,fall 1996 couture
'373': chanel,fall 1997 couture
'374': chanel,fall 1999 couture
'375': chanel,fall 2000 couture
'376': chanel,fall 2000 ready to wear
'377': chanel,fall 2002 couture
'378': chanel,fall 2003 ready to wear
'379': chanel,fall 2004 couture
'380': chanel,fall 2004 ready to wear
'381': chanel,fall 2005 couture
'382': chanel,fall 2005 ready to wear
'383': chanel,fall 2006 couture
'384': chanel,fall 2006 ready to wear
'385': chanel,fall 2007 couture
'386': chanel,fall 2007 ready to wear
'387': chanel,fall 2008 couture
'388': chanel,fall 2008 ready to wear
'389': chanel,fall 2009 couture
'390': chanel,fall 2009 ready to wear
'391': chanel,fall 2010 couture
'392': chanel,fall 2010 ready to wear
'393': chanel,fall 2011 couture
'394': chanel,fall 2011 ready to wear
'395': chanel,fall 2012 couture
'396': chanel,fall 2012 ready to wear
'397': chanel,fall 2013 couture
'398': chanel,fall 2013 ready to wear
'399': chanel,fall 2014 couture
'400': chanel,fall 2014 ready to wear
'401': chanel,fall 2015 couture
'402': chanel,fall 2015 ready to wear
'403': chanel,fall 2016 couture
'404': chanel,fall 2016 ready to wear
'405': chanel,fall 2017 couture
'406': chanel,fall 2017 ready to wear
'407': chanel,fall 2018 couture
'408': chanel,fall 2018 ready to wear
'409': chanel,fall 2019 couture
'410': chanel,fall 2019 ready to wear
'411': chanel,fall 2020 couture
'412': chanel,fall 2020 ready to wear
'413': chanel,fall 2021 couture
'414': chanel,fall 2021 ready to wear
'415': chanel,fall 2022 couture
'416': chanel,fall 2022 ready to wear
'417': chanel,fall 2023 couture
'418': chanel,fall 2023 ready to wear
'419': chanel,pre fall 2008
'420': chanel,pre fall 2009
'421': chanel,pre fall 2010
'422': chanel,pre fall 2011
'423': chanel,pre fall 2012
'424': chanel,pre fall 2013
'425': chanel,pre fall 2014
'426': chanel,pre fall 2015
'427': chanel,pre fall 2016
'428': chanel,pre fall 2017
'429': chanel,pre fall 2018
'430': chanel,pre fall 2019
'431': chanel,pre fall 2020
'432': chanel,pre fall 2021
'433': chanel,pre fall 2022
'434': chanel,pre fall 2023
'435': chanel,pre fall 2024
'436': chanel,resort 2007
'437': chanel,resort 2008
'438': chanel,resort 2009
'439': chanel,resort 2010
'440': chanel,resort 2011
'441': chanel,resort 2012
'442': chanel,resort 2013
'443': chanel,resort 2014
'444': chanel,resort 2015
'445': chanel,resort 2016
'446': chanel,resort 2017
'447': chanel,resort 2018
'448': chanel,resort 2019
'449': chanel,resort 2020
'450': chanel,resort 2021
'451': chanel,resort 2022
'452': chanel,resort 2023
'453': chanel,resort 2024
'454': chanel,spring 1992 ready to wear
'455': chanel,spring 1993 couture
'456': chanel,spring 1993 ready to wear
'457': chanel,spring 1994 ready to wear
'458': chanel,spring 1995 ready to wear
'459': chanel,spring 1996 ready to wear
'460': chanel,spring 1997 couture
'461': chanel,spring 1999 couture
'462': chanel,spring 2001 couture
'463': chanel,spring 2002 couture
'464': chanel,spring 2002 ready to wear
'465': chanel,spring 2003 couture
'466': chanel,spring 2004 couture
'467': chanel,spring 2004 ready to wear
'468': chanel,spring 2005 couture
'469': chanel,spring 2005 ready to wear
'470': chanel,spring 2006 couture
'471': chanel,spring 2006 ready to wear
'472': chanel,spring 2007 couture
'473': chanel,spring 2007 ready to wear
'474': chanel,spring 2008 couture
'475': chanel,spring 2008 ready to wear
'476': chanel,spring 2009 couture
'477': chanel,spring 2009 ready to wear
'478': chanel,spring 2010 couture
'479': chanel,spring 2010 ready to wear
'480': chanel,spring 2011 couture
'481': chanel,spring 2011 ready to wear
'482': chanel,spring 2012 couture
'483': chanel,spring 2012 ready to wear
'484': chanel,spring 2013 couture
'485': chanel,spring 2013 ready to wear
'486': chanel,spring 2014 couture
'487': chanel,spring 2014 ready to wear
'488': chanel,spring 2015 couture
'489': chanel,spring 2015 ready to wear
'490': chanel,spring 2016 couture
'491': chanel,spring 2016 ready to wear
'492': chanel,spring 2017 couture
'493': chanel,spring 2017 ready to wear
'494': chanel,spring 2018 couture
'495': chanel,spring 2018 ready to wear
'496': chanel,spring 2019 couture
'497': chanel,spring 2019 ready to wear
'498': chanel,spring 2020 couture
'499': chanel,spring 2020 ready to wear
'500': chanel,spring 2021 couture
'501': chanel,spring 2021 ready to wear
'502': chanel,spring 2022 couture
'503': chanel,spring 2022 ready to wear
'504': chanel,spring 2023 couture
'505': chanel,spring 2023 ready to wear
'506': chanel,spring 2024 ready to wear
'507': christian dior,fall 1999 couture
'508': christian dior,fall 2000 couture
'509': christian dior,fall 2000 ready to wear
'510': christian dior,fall 2001 couture
'511': christian dior,fall 2001 ready to wear
'512': christian dior,fall 2002 couture
'513': christian dior,fall 2002 ready to wear
'514': christian dior,fall 2003 couture
'515': christian dior,fall 2003 ready to wear
'516': christian dior,fall 2004 couture
'517': christian dior,fall 2004 ready to wear
'518': christian dior,fall 2005 couture
'519': christian dior,fall 2005 ready to wear
'520': christian dior,fall 2006 couture
'521': christian dior,fall 2006 ready to wear
'522': christian dior,fall 2007 couture
'523': christian dior,fall 2007 ready to wear
'524': christian dior,fall 2008 couture
'525': christian dior,fall 2008 ready to wear
'526': christian dior,fall 2009 couture
'527': christian dior,fall 2009 ready to wear
'528': christian dior,fall 2010 couture
'529': christian dior,fall 2010 menswear
'530': christian dior,fall 2010 ready to wear
'531': christian dior,fall 2011 couture
'532': christian dior,fall 2011 ready to wear
'533': christian dior,fall 2012 couture
'534': christian dior,fall 2012 ready to wear
'535': christian dior,fall 2013 couture
'536': christian dior,fall 2013 ready to wear
'537': christian dior,fall 2014 couture
'538': christian dior,fall 2014 ready to wear
'539': christian dior,fall 2015 couture
'540': christian dior,fall 2015 ready to wear
'541': christian dior,fall 2016 couture
'542': christian dior,fall 2016 ready to wear
'543': christian dior,fall 2017 couture
'544': christian dior,fall 2017 ready to wear
'545': christian dior,fall 2018 couture
'546': christian dior,fall 2018 ready to wear
'547': christian dior,fall 2019 couture
'548': christian dior,fall 2019 ready to wear
'549': christian dior,fall 2020 couture
'550': christian dior,fall 2021 couture
'551': christian dior,fall 2021 ready to wear
'552': christian dior,fall 2022 couture
'553': christian dior,fall 2022 ready to wear
'554': christian dior,fall 2023 couture
'555': christian dior,fall 2023 ready to wear
'556': christian dior,pre fall 2009
'557': christian dior,pre fall 2010
'558': christian dior,pre fall 2011
'559': christian dior,pre fall 2012
'560': christian dior,pre fall 2013
'561': christian dior,pre fall 2014
'562': christian dior,pre fall 2015
'563': christian dior,pre fall 2016
'564': christian dior,pre fall 2017
'565': christian dior,pre fall 2018
'566': christian dior,pre fall 2019
'567': christian dior,pre fall 2020
'568': christian dior,pre fall 2021
'569': christian dior,pre fall 2022
'570': christian dior,pre fall 2023
'571': christian dior,resort 2007
'572': christian dior,resort 2008
'573': christian dior,resort 2009
'574': christian dior,resort 2010
'575': christian dior,resort 2011
'576': christian dior,resort 2012
'577': christian dior,resort 2013
'578': christian dior,resort 2014
'579': christian dior,resort 2015
'580': christian dior,resort 2016
'581': christian dior,resort 2017
'582': christian dior,resort 2018
'583': christian dior,resort 2019
'584': christian dior,resort 2020
'585': christian dior,resort 2021
'586': christian dior,resort 2022
'587': christian dior,resort 2023
'588': christian dior,resort 2024
'589': christian dior,spring 1999 couture
'590': christian dior,spring 2000 ready to wear
'591': christian dior,spring 2001 couture
'592': christian dior,spring 2001 ready to wear
'593': christian dior,spring 2002 couture
'594': christian dior,spring 2002 ready to wear
'595': christian dior,spring 2003 couture
'596': christian dior,spring 2003 ready to wear
'597': christian dior,spring 2004 couture
'598': christian dior,spring 2004 ready to wear
'599': christian dior,spring 2005 couture
'600': christian dior,spring 2005 ready to wear
'601': christian dior,spring 2006 couture
'602': christian dior,spring 2006 ready to wear
'603': christian dior,spring 2007 couture
'604': christian dior,spring 2007 ready to wear
'605': christian dior,spring 2008 couture
'606': christian dior,spring 2008 ready to wear
'607': christian dior,spring 2009 couture
'608': christian dior,spring 2009 ready to wear
'609': christian dior,spring 2010 couture
'610': christian dior,spring 2010 menswear
'611': christian dior,spring 2010 ready to wear
'612': christian dior,spring 2011 couture
'613': christian dior,spring 2011 ready to wear
'614': christian dior,spring 2012 couture
'615': christian dior,spring 2012 ready to wear
'616': christian dior,spring 2013 couture
'617': christian dior,spring 2013 ready to wear
'618': christian dior,spring 2014 couture
'619': christian dior,spring 2014 ready to wear
'620': christian dior,spring 2015 couture
'621': christian dior,spring 2015 ready to wear
'622': christian dior,spring 2016 couture
'623': christian dior,spring 2016 ready to wear
'624': christian dior,spring 2017 couture
'625': christian dior,spring 2017 ready to wear
'626': christian dior,spring 2018 couture
'627': christian dior,spring 2018 ready to wear
'628': christian dior,spring 2019 couture
'629': christian dior,spring 2019 ready to wear
'630': christian dior,spring 2020 couture
'631': christian dior,spring 2020 ready to wear
'632': christian dior,spring 2021 couture
'633': christian dior,spring 2021 ready to wear
'634': christian dior,spring 2022 couture
'635': christian dior,spring 2022 ready to wear
'636': christian dior,spring 2023 couture
'637': christian dior,spring 2023 ready to wear
'638': christian dior,spring 2024 ready to wear
'639': fendi,fall 1999 ready to wear
'640': fendi,fall 2000 ready to wear
'641': fendi,fall 2001 ready to wear
'642': fendi,fall 2002 ready to wear
'643': fendi,fall 2003 ready to wear
'644': fendi,fall 2004 ready to wear
'645': fendi,fall 2005 ready to wear
'646': fendi,fall 2006 ready to wear
'647': fendi,fall 2007 menswear
'648': fendi,fall 2007 ready to wear
'649': fendi,fall 2008 menswear
'650': fendi,fall 2008 ready to wear
'651': fendi,fall 2009 ready to wear
'652': fendi,fall 2010 ready to wear
'653': fendi,fall 2011 ready to wear
'654': fendi,fall 2012 menswear
'655': fendi,fall 2012 ready to wear
'656': fendi,fall 2013 menswear
'657': fendi,fall 2013 ready to wear
'658': fendi,fall 2014 menswear
'659': fendi,fall 2014 ready to wear
'660': fendi,fall 2015 couture
'661': fendi,fall 2015 menswear
'662': fendi,fall 2015 ready to wear
'663': fendi,fall 2016 couture
'664': fendi,fall 2016 menswear
'665': fendi,fall 2016 ready to wear
'666': fendi,fall 2017 couture
'667': fendi,fall 2017 menswear
'668': fendi,fall 2017 ready to wear
'669': fendi,fall 2018 couture
'670': fendi,fall 2018 menswear
'671': fendi,fall 2018 ready to wear
'672': fendi,fall 2019 couture
'673': fendi,fall 2019 menswear
'674': fendi,fall 2019 ready to wear
'675': fendi,fall 2020 menswear
'676': fendi,fall 2020 ready to wear
'677': fendi,fall 2021 couture
'678': fendi,fall 2021 menswear
'679': fendi,fall 2021 ready to wear
'680': fendi,fall 2022 couture
'681': fendi,fall 2022 menswear
'682': fendi,fall 2022 ready to wear
'683': fendi,fall 2023 couture
'684': fendi,fall 2023 menswear
'685': fendi,fall 2023 ready to wear
'686': fendi,pre fall 2011
'687': fendi,pre fall 2012
'688': fendi,pre fall 2013
'689': fendi,pre fall 2014
'690': fendi,pre fall 2015
'691': fendi,pre fall 2016
'692': fendi,pre fall 2017
'693': fendi,pre fall 2018
'694': fendi,pre fall 2019
'695': fendi,pre fall 2020
'696': fendi,pre fall 2022
'697': fendi,resort 2008
'698': fendi,resort 2009
'699': fendi,resort 2012
'700': fendi,resort 2013
'701': fendi,resort 2014
'702': fendi,resort 2015
'703': fendi,resort 2016
'704': fendi,resort 2017
'705': fendi,resort 2018
'706': fendi,resort 2019
'707': fendi,resort 2020
'708': fendi,resort 2022
'709': fendi,resort 2023
'710': fendi,resort 2024
'711': fendi,spring 1999 ready to wear
'712': fendi,spring 2000 ready to wear
'713': fendi,spring 2001 ready to wear
'714': fendi,spring 2002 ready to wear
'715': fendi,spring 2003 ready to wear
'716': fendi,spring 2004 ready to wear
'717': fendi,spring 2005 ready to wear
'718': fendi,spring 2006 ready to wear
'719': fendi,spring 2007 ready to wear
'720': fendi,spring 2008 menswear
'721': fendi,spring 2008 ready to wear
'722': fendi,spring 2009 menswear
'723': fendi,spring 2009 ready to wear
'724': fendi,spring 2010 ready to wear
'725': fendi,spring 2011 ready to wear
'726': fendi,spring 2012 ready to wear
'727': fendi,spring 2013 menswear
'728': fendi,spring 2013 ready to wear
'729': fendi,spring 2014 menswear
'730': fendi,spring 2014 ready to wear
'731': fendi,spring 2015 menswear
'732': fendi,spring 2015 ready to wear
'733': fendi,spring 2016 menswear
'734': fendi,spring 2016 ready to wear
'735': fendi,spring 2017 menswear
'736': fendi,spring 2017 ready to wear
'737': fendi,spring 2018 menswear
'738': fendi,spring 2018 ready to wear
'739': fendi,spring 2019 menswear
'740': fendi,spring 2019 ready to wear
'741': fendi,spring 2020 menswear
'742': fendi,spring 2020 ready to wear
'743': fendi,spring 2021 couture
'744': fendi,spring 2021 menswear
'745': fendi,spring 2021 ready to wear
'746': fendi,spring 2022 couture
'747': fendi,spring 2022 menswear
'748': fendi,spring 2022 ready to wear
'749': fendi,spring 2023 couture
'750': fendi,spring 2023 menswear
'751': fendi,spring 2023 ready to wear
'752': fendi,spring 2024 menswear
'753': fendi,spring 2024 ready to wear
'754': gucci,fall 1995 ready to wear
'755': gucci,fall 1996 ready to wear
'756': gucci,fall 2000 ready to wear
'757': gucci,fall 2001 ready to wear
'758': gucci,fall 2002 ready to wear
'759': gucci,fall 2003 ready to wear
'760': gucci,fall 2004 ready to wear
'761': gucci,fall 2005 menswear
'762': gucci,fall 2005 ready to wear
'763': gucci,fall 2006 menswear
'764': gucci,fall 2006 ready to wear
'765': gucci,fall 2007 menswear
'766': gucci,fall 2007 ready to wear
'767': gucci,fall 2008 menswear
'768': gucci,fall 2008 ready to wear
'769': gucci,fall 2009 ready to wear
'770': gucci,fall 2010 menswear
'771': gucci,fall 2010 ready to wear
'772': gucci,fall 2011 menswear
'773': gucci,fall 2011 ready to wear
'774': gucci,fall 2012 menswear
'775': gucci,fall 2012 ready to wear
'776': gucci,fall 2013 menswear
'777': gucci,fall 2013 ready to wear
'778': gucci,fall 2014 menswear
'779': gucci,fall 2014 ready to wear
'780': gucci,fall 2015 menswear
'781': gucci,fall 2015 ready to wear
'782': gucci,fall 2016 menswear
'783': gucci,fall 2016 ready to wear
'784': gucci,fall 2017 menswear
'785': gucci,fall 2017 ready to wear
'786': gucci,fall 2018 menswear
'787': gucci,fall 2018 ready to wear
'788': gucci,fall 2019 menswear
'789': gucci,fall 2019 ready to wear
'790': gucci,fall 2020 menswear
'791': gucci,fall 2020 ready to wear
'792': gucci,fall 2022 ready to wear
'793': gucci,fall 2023 menswear
'794': gucci,fall 2023 ready to wear
'795': gucci,pre fall 2011
'796': gucci,pre fall 2012
'797': gucci,pre fall 2013
'798': gucci,pre fall 2014
'799': gucci,pre fall 2015
'800': gucci,pre fall 2016
'801': gucci,pre fall 2017
'802': gucci,pre fall 2018
'803': gucci,pre fall 2019
'804': gucci,pre fall 2020
'805': gucci,pre fall 2020 menswear
'806': gucci,pre fall 2021
'807': gucci,pre fall 2021 menswear
'808': gucci,pre fall 2022
'809': gucci,resort 2007
'810': gucci,resort 2008
'811': gucci,resort 2009
'812': gucci,resort 2010
'813': gucci,resort 2011
'814': gucci,resort 2012
'815': gucci,resort 2013
'816': gucci,resort 2014
'817': gucci,resort 2015
'818': gucci,resort 2016
'819': gucci,resort 2017
'820': gucci,resort 2018
'821': gucci,resort 2019
'822': gucci,resort 2020
'823': gucci,resort 2021
'824': gucci,resort 2023
'825': gucci,resort 2024
'826': gucci,spring 1999 ready to wear
'827': gucci,spring 2000 ready to wear
'828': gucci,spring 2001 ready to wear
'829': gucci,spring 2002 ready to wear
'830': gucci,spring 2003 ready to wear
'831': gucci,spring 2004 ready to wear
'832': gucci,spring 2005 menswear
'833': gucci,spring 2005 ready to wear
'834': gucci,spring 2006 menswear
'835': gucci,spring 2006 ready to wear
'836': gucci,spring 2007 menswear
'837': gucci,spring 2007 ready to wear
'838': gucci,spring 2008 menswear
'839': gucci,spring 2008 ready to wear
'840': gucci,spring 2009 menswear
'841': gucci,spring 2009 ready to wear
'842': gucci,spring 2010 menswear
'843': gucci,spring 2010 ready to wear
'844': gucci,spring 2011 menswear
'845': gucci,spring 2011 ready to wear
'846': gucci,spring 2012 menswear
'847': gucci,spring 2012 ready to wear
'848': gucci,spring 2013 menswear
'849': gucci,spring 2013 ready to wear
'850': gucci,spring 2014 menswear
'851': gucci,spring 2014 ready to wear
'852': gucci,spring 2015 menswear
'853': gucci,spring 2015 ready to wear
'854': gucci,spring 2016 menswear
'855': gucci,spring 2016 ready to wear
'856': gucci,spring 2017 menswear
'857': gucci,spring 2017 ready to wear
'858': gucci,spring 2018 menswear
'859': gucci,spring 2018 ready to wear
'860': gucci,spring 2019 ready to wear
'861': gucci,spring 2020 menswear
'862': gucci,spring 2020 ready to wear
'863': gucci,spring 2021 menswear
'864': gucci,spring 2021 ready to wear
'865': gucci,spring 2022 ready to wear
'866': gucci,spring 2023 ready to wear
'867': gucci,spring 2024 menswear
'868': gucci,spring 2024 ready to wear
'869': hermes,fall 1999 ready to wear
'870': hermes,fall 2000 ready to wear
'871': hermes,fall 2001 ready to wear
'872': hermes,fall 2004 ready to wear
'873': hermes,fall 2005 menswear
'874': hermes,fall 2005 ready to wear
'875': hermes,fall 2006 menswear
'876': hermes,fall 2006 ready to wear
'877': hermes,fall 2007 menswear
'878': hermes,fall 2007 ready to wear
'879': hermes,fall 2008 menswear
'880': hermes,fall 2008 ready to wear
'881': hermes,fall 2009 ready to wear
'882': hermes,fall 2010 menswear
'883': hermes,fall 2010 ready to wear
'884': hermes,fall 2011 menswear
'885': hermes,fall 2011 ready to wear
'886': hermes,fall 2012 menswear
'887': hermes,fall 2012 ready to wear
'888': hermes,fall 2013 menswear
'889': hermes,fall 2013 ready to wear
'890': hermes,fall 2014 menswear
'891': hermes,fall 2014 ready to wear
'892': hermes,fall 2015 menswear
'893': hermes,fall 2015 ready to wear
'894': hermes,fall 2016 menswear
'895': hermes,fall 2016 ready to wear
'896': hermes,fall 2017 menswear
'897': hermes,fall 2017 ready to wear
'898': hermes,fall 2018 menswear
'899': hermes,fall 2018 ready to wear
'900': hermes,fall 2019 menswear
'901': hermes,fall 2019 ready to wear
'902': hermes,fall 2020 menswear
'903': hermes,fall 2020 ready to wear
'904': hermes,fall 2021 menswear
'905': hermes,fall 2021 ready to wear
'906': hermes,fall 2022 menswear
'907': hermes,fall 2022 ready to wear
'908': hermes,fall 2023 menswear
'909': hermes,fall 2023 ready to wear
'910': hermes,pre fall 2017
'911': hermes,pre fall 2018
'912': hermes,pre fall 2019
'913': hermes,resort 2017
'914': hermes,resort 2018
'915': hermes,resort 2019
'916': hermes,spring 1999 ready to wear
'917': hermes,spring 2000 ready to wear
'918': hermes,spring 2001 ready to wear
'919': hermes,spring 2002 ready to wear
'920': hermes,spring 2006 menswear
'921': hermes,spring 2006 ready to wear
'922': hermes,spring 2007 menswear
'923': hermes,spring 2007 ready to wear
'924': hermes,spring 2008 menswear
'925': hermes,spring 2008 ready to wear
'926': hermes,spring 2009 menswear
'927': hermes,spring 2010 menswear
'928': hermes,spring 2010 ready to wear
'929': hermes,spring 2011 menswear
'930': hermes,spring 2011 ready to wear
'931': hermes,spring 2012 menswear
'932': hermes,spring 2012 ready to wear
'933': hermes,spring 2013 menswear
'934': hermes,spring 2013 ready to wear
'935': hermes,spring 2014 menswear
'936': hermes,spring 2014 ready to wear
'937': hermes,spring 2015 menswear
'938': hermes,spring 2015 ready to wear
'939': hermes,spring 2016 menswear
'940': hermes,spring 2016 ready to wear
'941': hermes,spring 2017 menswear
'942': hermes,spring 2017 ready to wear
'943': hermes,spring 2018 menswear
'944': hermes,spring 2018 ready to wear
'945': hermes,spring 2019 menswear
'946': hermes,spring 2019 ready to wear
'947': hermes,spring 2020 menswear
'948': hermes,spring 2020 ready to wear
'949': hermes,spring 2021 menswear
'950': hermes,spring 2021 ready to wear
'951': hermes,spring 2022 menswear
'952': hermes,spring 2022 ready to wear
'953': hermes,spring 2023 menswear
'954': hermes,spring 2023 ready to wear
'955': hermes,spring 2024 menswear
'956': hermes,spring 2024 ready to wear
'957': louis vuitton,fall 1998 ready to wear
'958': louis vuitton,fall 2000 ready to wear
'959': louis vuitton,fall 2001 ready to wear
'960': louis vuitton,fall 2002 ready to wear
'961': louis vuitton,fall 2003 ready to wear
'962': louis vuitton,fall 2004 ready to wear
'963': louis vuitton,fall 2005 menswear
'964': louis vuitton,fall 2005 ready to wear
'965': louis vuitton,fall 2006 menswear
'966': louis vuitton,fall 2006 ready to wear
'967': louis vuitton,fall 2007 menswear
'968': louis vuitton,fall 2008 menswear
'969': louis vuitton,fall 2008 ready to wear
'970': louis vuitton,fall 2009 ready to wear
'971': louis vuitton,fall 2010 menswear
'972': louis vuitton,fall 2010 ready to wear
'973': louis vuitton,fall 2011 menswear
'974': louis vuitton,fall 2011 ready to wear
'975': louis vuitton,fall 2012 menswear
'976': louis vuitton,fall 2012 ready to wear
'977': louis vuitton,fall 2013 menswear
'978': louis vuitton,fall 2013 ready to wear
'979': louis vuitton,fall 2014 menswear
'980': louis vuitton,fall 2014 ready to wear
'981': louis vuitton,fall 2015 menswear
'982': louis vuitton,fall 2015 ready to wear
'983': louis vuitton,fall 2016 menswear
'984': louis vuitton,fall 2016 ready to wear
'985': louis vuitton,fall 2017 menswear
'986': louis vuitton,fall 2017 ready to wear
'987': louis vuitton,fall 2018 menswear
'988': louis vuitton,fall 2018 ready to wear
'989': louis vuitton,fall 2019 menswear
'990': louis vuitton,fall 2019 ready to wear
'991': louis vuitton,fall 2020 menswear
'992': louis vuitton,fall 2020 ready to wear
'993': louis vuitton,fall 2021 menswear
'994': louis vuitton,fall 2021 ready to wear
'995': louis vuitton,fall 2022 menswear
'996': louis vuitton,fall 2022 ready to wear
'997': louis vuitton,fall 2023 menswear
'998': louis vuitton,fall 2023 ready to wear
'999': louis vuitton,pre fall 2008
'1000': louis vuitton,pre fall 2009
'1001': louis vuitton,pre fall 2010
'1002': louis vuitton,pre fall 2011
'1003': louis vuitton,pre fall 2012
'1004': louis vuitton,pre fall 2013
'1005': louis vuitton,pre fall 2014
'1006': louis vuitton,pre fall 2015
'1007': louis vuitton,pre fall 2016
'1008': louis vuitton,pre fall 2017
'1009': louis vuitton,pre fall 2018
'1010': louis vuitton,pre fall 2019
'1011': louis vuitton,pre fall 2020
'1012': louis vuitton,pre fall 2020 menswear
'1013': louis vuitton,pre fall 2021
'1014': louis vuitton,pre fall 2021 menswear
'1015': louis vuitton,pre fall 2022 menswear
'1016': louis vuitton,pre fall 2023
'1017': louis vuitton,pre fall 2023 menswear
'1018': louis vuitton,pre fall 2024 menswear
'1019': louis vuitton,resort 2008
'1020': louis vuitton,resort 2009
'1021': louis vuitton,resort 2010
'1022': louis vuitton,resort 2011
'1023': louis vuitton,resort 2012
'1024': louis vuitton,resort 2013
'1025': louis vuitton,resort 2014
'1026': louis vuitton,resort 2015
'1027': louis vuitton,resort 2016
'1028': louis vuitton,resort 2017
'1029': louis vuitton,resort 2018
'1030': louis vuitton,resort 2019
'1031': louis vuitton,resort 2020
'1032': louis vuitton,resort 2021
'1033': louis vuitton,resort 2021 menswear
'1034': louis vuitton,resort 2022
'1035': louis vuitton,resort 2022 menswear
'1036': louis vuitton,resort 2023
'1037': louis vuitton,resort 2023 menswear
'1038': louis vuitton,resort 2024
'1039': louis vuitton,resort 2024 menswear
'1040': louis vuitton,spring 2000 ready to wear
'1041': louis vuitton,spring 2001 ready to wear
'1042': louis vuitton,spring 2002 ready to wear
'1043': louis vuitton,spring 2003 ready to wear
'1044': louis vuitton,spring 2004 ready to wear
'1045': louis vuitton,spring 2005 menswear
'1046': louis vuitton,spring 2005 ready to wear
'1047': louis vuitton,spring 2006 menswear
'1048': louis vuitton,spring 2006 ready to wear
'1049': louis vuitton,spring 2007 menswear
'1050': louis vuitton,spring 2007 ready to wear
'1051': louis vuitton,spring 2008 menswear
'1052': louis vuitton,spring 2008 ready to wear
'1053': louis vuitton,spring 2009 menswear
'1054': louis vuitton,spring 2009 ready to wear
'1055': louis vuitton,spring 2010 menswear
'1056': louis vuitton,spring 2010 ready to wear
'1057': louis vuitton,spring 2011 menswear
'1058': louis vuitton,spring 2011 ready to wear
'1059': louis vuitton,spring 2012 menswear
'1060': louis vuitton,spring 2012 ready to wear
'1061': louis vuitton,spring 2013 menswear
'1062': louis vuitton,spring 2013 ready to wear
'1063': louis vuitton,spring 2014 menswear
'1064': louis vuitton,spring 2014 ready to wear
'1065': louis vuitton,spring 2015 menswear
'1066': louis vuitton,spring 2015 ready to wear
'1067': louis vuitton,spring 2016 menswear
'1068': louis vuitton,spring 2016 ready to wear
'1069': louis vuitton,spring 2017 menswear
'1070': louis vuitton,spring 2017 ready to wear
'1071': louis vuitton,spring 2018 menswear
'1072': louis vuitton,spring 2018 ready to wear
'1073': louis vuitton,spring 2019 menswear
'1074': louis vuitton,spring 2019 ready to wear
'1075': louis vuitton,spring 2020 menswear
'1076': louis vuitton,spring 2020 ready to wear
'1077': louis vuitton,spring 2021 menswear
'1078': louis vuitton,spring 2021 ready to wear
'1079': louis vuitton,spring 2022 menswear
'1080': louis vuitton,spring 2023 menswear
'1081': louis vuitton,spring 2023 ready to wear
'1082': louis vuitton,spring 2024 menswear
'1083': prada,fall 1996 ready to wear
'1084': prada,fall 2000 ready to wear
'1085': prada,fall 2001 ready to wear
'1086': prada,fall 2002 ready to wear
'1087': prada,fall 2003 ready to wear
'1088': prada,fall 2004 ready to wear
'1089': prada,fall 2005 menswear
'1090': prada,fall 2005 ready to wear
'1091': prada,fall 2006 menswear
'1092': prada,fall 2006 ready to wear
'1093': prada,fall 2007 menswear
'1094': prada,fall 2007 ready to wear
'1095': prada,fall 2008 menswear
'1096': prada,fall 2008 ready to wear
'1097': prada,fall 2009 menswear
'1098': prada,fall 2009 ready to wear
'1099': prada,fall 2010 menswear
'1100': prada,fall 2010 ready to wear
'1101': prada,fall 2011 menswear
'1102': prada,fall 2011 ready to wear
'1103': prada,fall 2012 menswear
'1104': prada,fall 2012 ready to wear
'1105': prada,fall 2013 menswear
'1106': prada,fall 2013 ready to wear
'1107': prada,fall 2014 menswear
'1108': prada,fall 2014 ready to wear
'1109': prada,fall 2015 menswear
'1110': prada,fall 2015 ready to wear
'1111': prada,fall 2016 menswear
'1112': prada,fall 2016 ready to wear
'1113': prada,fall 2017 menswear
'1114': prada,fall 2017 ready to wear
'1115': prada,fall 2018 menswear
'1116': prada,fall 2018 ready to wear
'1117': prada,fall 2019 menswear
'1118': prada,fall 2019 ready to wear
'1119': prada,fall 2020 menswear
'1120': prada,fall 2020 ready to wear
'1121': prada,fall 2021 menswear
'1122': prada,fall 2021 ready to wear
'1123': prada,fall 2022 menswear
'1124': prada,fall 2022 ready to wear
'1125': prada,fall 2023 menswear
'1126': prada,fall 2023 ready to wear
'1127': prada,pre fall 2009
'1128': prada,pre fall 2010
'1129': prada,resort 2008
'1130': prada,resort 2009
'1131': prada,resort 2010
'1132': prada,resort 2011
'1133': prada,resort 2012
'1134': prada,resort 2013
'1135': prada,resort 2018
'1136': prada,resort 2019
'1137': prada,resort 2020
'1138': prada,spring 1992 ready to wear
'1139': prada,spring 1993 ready to wear
'1140': prada,spring 1994 ready to wear
'1141': prada,spring 1995 ready to wear
'1142': prada,spring 1996 ready to wear
'1143': prada,spring 1997 ready to wear
'1144': prada,spring 1998 ready to wear
'1145': prada,spring 1999 ready to wear
'1146': prada,spring 2000 ready to wear
'1147': prada,spring 2001 ready to wear
'1148': prada,spring 2002 ready to wear
'1149': prada,spring 2003 ready to wear
'1150': prada,spring 2004 ready to wear
'1151': prada,spring 2005 menswear
'1152': prada,spring 2005 ready to wear
'1153': prada,spring 2006 menswear
'1154': prada,spring 2006 ready to wear
'1155': prada,spring 2007 menswear
'1156': prada,spring 2007 ready to wear
'1157': prada,spring 2008 menswear
'1158': prada,spring 2008 ready to wear
'1159': prada,spring 2009 menswear
'1160': prada,spring 2009 ready to wear
'1161': prada,spring 2010 ready to wear
'1162': prada,spring 2011 menswear
'1163': prada,spring 2011 ready to wear
'1164': prada,spring 2012 menswear
'1165': prada,spring 2012 ready to wear
'1166': prada,spring 2013 menswear
'1167': prada,spring 2013 ready to wear
'1168': prada,spring 2014 menswear
'1169': prada,spring 2014 ready to wear
'1170': prada,spring 2015 menswear
'1171': prada,spring 2015 ready to wear
'1172': prada,spring 2016 menswear
'1173': prada,spring 2016 ready to wear
'1174': prada,spring 2017 menswear
'1175': prada,spring 2017 ready to wear
'1176': prada,spring 2018 menswear
'1177': prada,spring 2018 ready to wear
'1178': prada,spring 2019 menswear
'1179': prada,spring 2019 ready to wear
'1180': prada,spring 2020 menswear
'1181': prada,spring 2020 ready to wear
'1182': prada,spring 2021 menswear
'1183': prada,spring 2021 ready to wear
'1184': prada,spring 2022 menswear
'1185': prada,spring 2022 ready to wear
'1186': prada,spring 2023 menswear
'1187': prada,spring 2023 ready to wear
'1188': prada,spring 2024 menswear
'1189': prada,spring 2024 ready to wear
'1190': ralph lauren,fall 2000 ready to wear
'1191': ralph lauren,fall 2001 ready to wear
'1192': ralph lauren,fall 2002 ready to wear
'1193': ralph lauren,fall 2003 ready to wear
'1194': ralph lauren,fall 2004 ready to wear
'1195': ralph lauren,fall 2005 menswear
'1196': ralph lauren,fall 2005 ready to wear
'1197': ralph lauren,fall 2006 menswear
'1198': ralph lauren,fall 2006 ready to wear
'1199': ralph lauren,fall 2007 menswear
'1200': ralph lauren,fall 2007 ready to wear
'1201': ralph lauren,fall 2008 menswear
'1202': ralph lauren,fall 2008 ready to wear
'1203': ralph lauren,fall 2009 ready to wear
'1204': ralph lauren,fall 2010 menswear
'1205': ralph lauren,fall 2010 ready to wear
'1206': ralph lauren,fall 2011 ready to wear
'1207': ralph lauren,fall 2012 ready to wear
'1208': ralph lauren,fall 2013 menswear
'1209': ralph lauren,fall 2013 ready to wear
'1210': ralph lauren,fall 2014 menswear
'1211': ralph lauren,fall 2014 ready to wear
'1212': ralph lauren,fall 2015 menswear
'1213': ralph lauren,fall 2015 ready to wear
'1214': ralph lauren,fall 2016 menswear
'1215': ralph lauren,fall 2016 ready to wear
'1216': ralph lauren,fall 2017 menswear
'1217': ralph lauren,fall 2017 ready to wear
'1218': ralph lauren,fall 2018 menswear
'1219': ralph lauren,fall 2018 ready to wear
'1220': ralph lauren,fall 2019 menswear
'1221': ralph lauren,fall 2019 ready to wear
'1222': ralph lauren,fall 2020 menswear
'1223': ralph lauren,fall 2020 ready to wear
'1224': ralph lauren,fall 2021 ready to wear
'1225': ralph lauren,fall 2022 ready to wear
'1226': ralph lauren,fall 2023 ready to wear
'1227': ralph lauren,pre fall 2014
'1228': ralph lauren,pre fall 2015
'1229': ralph lauren,pre fall 2016
'1230': ralph lauren,pre fall 2017
'1231': ralph lauren,pre fall 2018
'1232': ralph lauren,pre fall 2019
'1233': ralph lauren,pre fall 2020
'1234': ralph lauren,pre fall 2021
'1235': ralph lauren,resort 2008
'1236': ralph lauren,resort 2009
'1237': ralph lauren,resort 2013
'1238': ralph lauren,resort 2014
'1239': ralph lauren,resort 2015
'1240': ralph lauren,resort 2016
'1241': ralph lauren,resort 2019
'1242': ralph lauren,resort 2022
'1243': ralph lauren,resort 2024
'1244': ralph lauren,spring 2000 ready to wear
'1245': ralph lauren,spring 2001 ready to wear
'1246': ralph lauren,spring 2002 ready to wear
'1247': ralph lauren,spring 2003 ready to wear
'1248': ralph lauren,spring 2004 ready to wear
'1249': ralph lauren,spring 2005 ready to wear
'1250': ralph lauren,spring 2006 menswear
'1251': ralph lauren,spring 2006 ready to wear
'1252': ralph lauren,spring 2007 menswear
'1253': ralph lauren,spring 2007 ready to wear
'1254': ralph lauren,spring 2008 menswear
'1255': ralph lauren,spring 2008 ready to wear
'1256': ralph lauren,spring 2009 ready to wear
'1257': ralph lauren,spring 2010 ready to wear
'1258': ralph lauren,spring 2011 ready to wear
'1259': ralph lauren,spring 2012 ready to wear
'1260': ralph lauren,spring 2013 menswear
'1261': ralph lauren,spring 2013 ready to wear
'1262': ralph lauren,spring 2014 menswear
'1263': ralph lauren,spring 2014 ready to wear
'1264': ralph lauren,spring 2015 menswear
'1265': ralph lauren,spring 2015 ready to wear
'1266': ralph lauren,spring 2016 menswear
'1267': ralph lauren,spring 2016 ready to wear
'1268': ralph lauren,spring 2017 menswear
'1269': ralph lauren,spring 2017 ready to wear
'1270': ralph lauren,spring 2018 menswear
'1271': ralph lauren,spring 2018 ready to wear
'1272': ralph lauren,spring 2019 menswear
'1273': ralph lauren,spring 2019 ready to wear
'1274': ralph lauren,spring 2020 menswear
'1275': ralph lauren,spring 2021 ready to wear
'1276': ralph lauren,spring 2022 ready to wear
'1277': ralph lauren,spring 2023 ready to wear
'1278': ralph lauren,spring 2024 menswear
'1279': ralph lauren,spring 2024 ready to wear
'1280': saint laurent,fall 2000 ready to wear
'1281': saint laurent,fall 2001 couture
'1282': saint laurent,fall 2001 ready to wear
'1283': saint laurent,fall 2002 ready to wear
'1284': saint laurent,fall 2003 ready to wear
'1285': saint laurent,fall 2004 ready to wear
'1286': saint laurent,fall 2005 menswear
'1287': saint laurent,fall 2005 ready to wear
'1288': saint laurent,fall 2006 menswear
'1289': saint laurent,fall 2006 ready to wear
'1290': saint laurent,fall 2007 menswear
'1291': saint laurent,fall 2007 ready to wear
'1292': saint laurent,fall 2008 menswear
'1293': saint laurent,fall 2008 ready to wear
'1294': saint laurent,fall 2009 ready to wear
'1295': saint laurent,fall 2010 menswear
'1296': saint laurent,fall 2010 ready to wear
'1297': saint laurent,fall 2011 menswear
'1298': saint laurent,fall 2011 ready to wear
'1299': saint laurent,fall 2012 menswear
'1300': saint laurent,fall 2012 ready to wear
'1301': saint laurent,fall 2013 menswear
'1302': saint laurent,fall 2013 ready to wear
'1303': saint laurent,fall 2014 menswear
'1304': saint laurent,fall 2014 ready to wear
'1305': saint laurent,fall 2015 menswear
'1306': saint laurent,fall 2015 ready to wear
'1307': saint laurent,fall 2016 menswear
'1308': saint laurent,fall 2016 ready to wear
'1309': saint laurent,fall 2017 ready to wear
'1310': saint laurent,fall 2018 ready to wear
'1311': saint laurent,fall 2019 menswear
'1312': saint laurent,fall 2019 ready to wear
'1313': saint laurent,fall 2020 ready to wear
'1314': saint laurent,fall 2021 menswear
'1315': saint laurent,fall 2021 ready to wear
'1316': saint laurent,fall 2022 menswear
'1317': saint laurent,fall 2022 ready to wear
'1318': saint laurent,fall 2023 menswear
'1319': saint laurent,fall 2023 ready to wear
'1320': saint laurent,pre fall 2009
'1321': saint laurent,pre fall 2010
'1322': saint laurent,pre fall 2011
'1323': saint laurent,pre fall 2012
'1324': saint laurent,pre fall 2013
'1325': saint laurent,pre fall 2016
'1326': saint laurent,pre fall 2019
'1327': saint laurent,pre fall 2020
'1328': saint laurent,pre fall 2020 menswear
'1329': saint laurent,pre fall 2021
'1330': saint laurent,pre fall 2022
'1331': saint laurent,pre fall 2023
'1332': saint laurent,resort 2008
'1333': saint laurent,resort 2010
'1334': saint laurent,resort 2011
'1335': saint laurent,resort 2012
'1336': saint laurent,resort 2014
'1337': saint laurent,resort 2020
'1338': saint laurent,resort 2021
'1339': saint laurent,resort 2022
'1340': saint laurent,resort 2023
'1341': saint laurent,spring 2000 ready to wear
'1342': saint laurent,spring 2001 couture
'1343': saint laurent,spring 2001 ready to wear
'1344': saint laurent,spring 2002 couture
'1345': saint laurent,spring 2002 ready to wear
'1346': saint laurent,spring 2003 ready to wear
'1347': saint laurent,spring 2004 ready to wear
'1348': saint laurent,spring 2005 menswear
'1349': saint laurent,spring 2005 ready to wear
'1350': saint laurent,spring 2006 menswear
'1351': saint laurent,spring 2006 ready to wear
'1352': saint laurent,spring 2007 menswear
'1353': saint laurent,spring 2007 ready to wear
'1354': saint laurent,spring 2008 menswear
'1355': saint laurent,spring 2008 ready to wear
'1356': saint laurent,spring 2009 menswear
'1357': saint laurent,spring 2009 ready to wear
'1358': saint laurent,spring 2010 ready to wear
'1359': saint laurent,spring 2011 menswear
'1360': saint laurent,spring 2011 ready to wear
'1361': saint laurent,spring 2012 menswear
'1362': saint laurent,spring 2012 ready to wear
'1363': saint laurent,spring 2013 ready to wear
'1364': saint laurent,spring 2014 menswear
'1365': saint laurent,spring 2014 ready to wear
'1366': saint laurent,spring 2015 menswear
'1367': saint laurent,spring 2015 ready to wear
'1368': saint laurent,spring 2016 menswear
'1369': saint laurent,spring 2016 ready to wear
'1370': saint laurent,spring 2017 ready to wear
'1371': saint laurent,spring 2018 ready to wear
'1372': saint laurent,spring 2019 menswear
'1373': saint laurent,spring 2019 ready to wear
'1374': saint laurent,spring 2020 menswear
'1375': saint laurent,spring 2020 ready to wear
'1376': saint laurent,spring 2021 menswear
'1377': saint laurent,spring 2021 ready to wear
'1378': saint laurent,spring 2022 menswear
'1379': saint laurent,spring 2022 ready to wear
'1380': saint laurent,spring 2023 menswear
'1381': saint laurent,spring 2023 ready to wear
'1382': saint laurent,spring 2024 menswear
'1383': saint laurent,spring 2024 ready to wear
'1384': valentino,fall 2000 ready to wear
'1385': valentino,fall 2001 couture
'1386': valentino,fall 2001 ready to wear
'1387': valentino,fall 2002 couture
'1388': valentino,fall 2002 ready to wear
'1389': valentino,fall 2003 couture
'1390': valentino,fall 2003 ready to wear
'1391': valentino,fall 2004 couture
'1392': valentino,fall 2004 ready to wear
'1393': valentino,fall 2005 couture
'1394': valentino,fall 2005 menswear
'1395': valentino,fall 2005 ready to wear
'1396': valentino,fall 2006 couture
'1397': valentino,fall 2006 menswear
'1398': valentino,fall 2006 ready to wear
'1399': valentino,fall 2007 couture
'1400': valentino,fall 2007 menswear
'1401': valentino,fall 2007 ready to wear
'1402': valentino,fall 2008 couture
'1403': valentino,fall 2008 menswear
'1404': valentino,fall 2008 ready to wear
'1405': valentino,fall 2009 couture
'1406': valentino,fall 2009 ready to wear
'1407': valentino,fall 2010 couture
'1408': valentino,fall 2010 ready to wear
'1409': valentino,fall 2011 couture
'1410': valentino,fall 2011 ready to wear
'1411': valentino,fall 2012 couture
'1412': valentino,fall 2012 menswear
'1413': valentino,fall 2012 ready to wear
'1414': valentino,fall 2013 couture
'1415': valentino,fall 2013 menswear
'1416': valentino,fall 2013 ready to wear
'1417': valentino,fall 2014 couture
'1418': valentino,fall 2014 menswear
'1419': valentino,fall 2014 ready to wear
'1420': valentino,fall 2015 couture
'1421': valentino,fall 2015 menswear
'1422': valentino,fall 2015 ready to wear
'1423': valentino,fall 2016 couture
'1424': valentino,fall 2016 menswear
'1425': valentino,fall 2016 ready to wear
'1426': valentino,fall 2017 couture
'1427': valentino,fall 2017 menswear
'1428': valentino,fall 2017 ready to wear
'1429': valentino,fall 2018 couture
'1430': valentino,fall 2018 menswear
'1431': valentino,fall 2018 ready to wear
'1432': valentino,fall 2019 couture
'1433': valentino,fall 2019 menswear
'1434': valentino,fall 2019 ready to wear
'1435': valentino,fall 2020 couture
'1436': valentino,fall 2020 menswear
'1437': valentino,fall 2020 ready to wear
'1438': valentino,fall 2021 couture
'1439': valentino,fall 2021 ready to wear
'1440': valentino,fall 2022 couture
'1441': valentino,fall 2022 ready to wear
'1442': valentino,fall 2023 couture
'1443': valentino,fall 2023 ready to wear
'1444': valentino,pre fall 2008
'1445': valentino,pre fall 2010
'1446': valentino,pre fall 2011
'1447': valentino,pre fall 2012
'1448': valentino,pre fall 2013
'1449': valentino,pre fall 2014
'1450': valentino,pre fall 2015
'1451': valentino,pre fall 2016
'1452': valentino,pre fall 2017
'1453': valentino,pre fall 2018
'1454': valentino,pre fall 2019
'1455': valentino,pre fall 2020
'1456': valentino,pre fall 2021
'1457': valentino,pre fall 2022
'1458': valentino,pre fall 2023
'1459': valentino,pre fall 2024
'1460': valentino,resort 2008
'1461': valentino,resort 2009
'1462': valentino,resort 2011
'1463': valentino,resort 2012
'1464': valentino,resort 2013
'1465': valentino,resort 2014
'1466': valentino,resort 2015
'1467': valentino,resort 2016
'1468': valentino,resort 2017
'1469': valentino,resort 2018
'1470': valentino,resort 2019
'1471': valentino,resort 2020
'1472': valentino,resort 2021
'1473': valentino,resort 2022
'1474': valentino,resort 2023
'1475': valentino,resort 2024
'1476': valentino,spring 2000 ready to wear
'1477': valentino,spring 2001 couture
'1478': valentino,spring 2001 ready to wear
'1479': valentino,spring 2002 couture
'1480': valentino,spring 2002 ready to wear
'1481': valentino,spring 2003 couture
'1482': valentino,spring 2003 ready to wear
'1483': valentino,spring 2004 couture
'1484': valentino,spring 2004 ready to wear
'1485': valentino,spring 2005 couture
'1486': valentino,spring 2005 menswear
'1487': valentino,spring 2005 ready to wear
'1488': valentino,spring 2006 couture
'1489': valentino,spring 2006 menswear
'1490': valentino,spring 2006 ready to wear
'1491': valentino,spring 2007 couture
'1492': valentino,spring 2007 menswear
'1493': valentino,spring 2007 ready to wear
'1494': valentino,spring 2008 couture
'1495': valentino,spring 2008 menswear
'1496': valentino,spring 2008 ready to wear
'1497': valentino,spring 2009 couture
'1498': valentino,spring 2009 menswear
'1499': valentino,spring 2009 ready to wear
'1500': valentino,spring 2010 couture
'1501': valentino,spring 2010 ready to wear
'1502': valentino,spring 2011 couture
'1503': valentino,spring 2011 ready to wear
'1504': valentino,spring 2012 couture
'1505': valentino,spring 2012 menswear
'1506': valentino,spring 2012 ready to wear
'1507': valentino,spring 2013 couture
'1508': valentino,spring 2013 menswear
'1509': valentino,spring 2013 ready to wear
'1510': valentino,spring 2014 couture
'1511': valentino,spring 2014 menswear
'1512': valentino,spring 2014 ready to wear
'1513': valentino,spring 2015 couture
'1514': valentino,spring 2015 menswear
'1515': valentino,spring 2015 ready to wear
'1516': valentino,spring 2016 couture
'1517': valentino,spring 2016 menswear
'1518': valentino,spring 2016 ready to wear
'1519': valentino,spring 2017 couture
'1520': valentino,spring 2017 menswear
'1521': valentino,spring 2017 ready to wear
'1522': valentino,spring 2018 couture
'1523': valentino,spring 2018 menswear
'1524': valentino,spring 2018 ready to wear
'1525': valentino,spring 2019 couture
'1526': valentino,spring 2019 menswear
'1527': valentino,spring 2019 ready to wear
'1528': valentino,spring 2020 couture
'1529': valentino,spring 2020 menswear
'1530': valentino,spring 2020 ready to wear
'1531': valentino,spring 2021 couture
'1532': valentino,spring 2021 menswear
'1533': valentino,spring 2021 ready to wear
'1534': valentino,spring 2022 couture
'1535': valentino,spring 2022 ready to wear
'1536': valentino,spring 2023 couture
'1537': valentino,spring 2023 ready to wear
'1538': valentino,spring 2024 menswear
'1539': versace by fendi,pre fall 2022
'1540': versace,fall 1991 ready to wear
'1541': versace,fall 1992 ready to wear
'1542': versace,fall 1993 ready to wear
'1543': versace,fall 1994 ready to wear
'1544': versace,fall 1995 ready to wear
'1545': versace,fall 1996 ready to wear
'1546': versace,fall 1997 ready to wear
'1547': versace,fall 2000 ready to wear
'1548': versace,fall 2001 couture
'1549': versace,fall 2001 ready to wear
'1550': versace,fall 2002 couture
'1551': versace,fall 2002 ready to wear
'1552': versace,fall 2003 couture
'1553': versace,fall 2003 ready to wear
'1554': versace,fall 2004 ready to wear
'1555': versace,fall 2005 menswear
'1556': versace,fall 2005 ready to wear
'1557': versace,fall 2006 menswear
'1558': versace,fall 2006 ready to wear
'1559': versace,fall 2007 menswear
'1560': versace,fall 2007 ready to wear
'1561': versace,fall 2008 menswear
'1562': versace,fall 2008 ready to wear
'1563': versace,fall 2009 ready to wear
'1564': versace,fall 2010 menswear
'1565': versace,fall 2010 ready to wear
'1566': versace,fall 2011 menswear
'1567': versace,fall 2011 ready to wear
'1568': versace,fall 2012 menswear
'1569': versace,fall 2012 ready to wear
'1570': versace,fall 2013 menswear
'1571': versace,fall 2013 ready to wear
'1572': versace,fall 2014 menswear
'1573': versace,fall 2014 ready to wear
'1574': versace,fall 2015 menswear
'1575': versace,fall 2015 ready to wear
'1576': versace,fall 2016 menswear
'1577': versace,fall 2016 ready to wear
'1578': versace,fall 2017 menswear
'1579': versace,fall 2017 ready to wear
'1580': versace,fall 2018 menswear
'1581': versace,fall 2018 ready to wear
'1582': versace,fall 2019 menswear
'1583': versace,fall 2019 ready to wear
'1584': versace,fall 2020 menswear
'1585': versace,fall 2020 ready to wear
'1586': versace,fall 2021 ready to wear
'1587': versace,fall 2022 menswear
'1588': versace,fall 2022 ready to wear
'1589': versace,fall 2023 ready to wear
'1590': versace,pre fall 2008
'1591': versace,pre fall 2009
'1592': versace,pre fall 2010
'1593': versace,pre fall 2011
'1594': versace,pre fall 2012
'1595': versace,pre fall 2013
'1596': versace,pre fall 2014
'1597': versace,pre fall 2015
'1598': versace,pre fall 2016
'1599': versace,pre fall 2017
'1600': versace,pre fall 2018
'1601': versace,pre fall 2019
'1602': versace,pre fall 2020
'1603': versace,pre fall 2021
'1604': versace,pre fall 2022
'1605': versace,pre fall 2022 menswear
'1606': versace,pre fall 2023
'1607': versace,resort 2008
'1608': versace,resort 2009
'1609': versace,resort 2010
'1610': versace,resort 2011
'1611': versace,resort 2012
'1612': versace,resort 2013
'1613': versace,resort 2014
'1614': versace,resort 2015
'1615': versace,resort 2016
'1616': versace,resort 2017
'1617': versace,resort 2018
'1618': versace,resort 2019
'1619': versace,resort 2020
'1620': versace,resort 2021
'1621': versace,resort 2022
'1622': versace,resort 2023
'1623': versace,spring 1991 ready to wear
'1624': versace,spring 1992 ready to wear
'1625': versace,spring 1993 ready to wear
'1626': versace,spring 1994 ready to wear
'1627': versace,spring 1995 ready to wear
'1628': versace,spring 1996 ready to wear
'1629': versace,spring 1997 ready to wear
'1630': versace,spring 2000 ready to wear
'1631': versace,spring 2001 couture
'1632': versace,spring 2001 ready to wear
'1633': versace,spring 2002 couture
'1634': versace,spring 2002 ready to wear
'1635': versace,spring 2003 couture
'1636': versace,spring 2003 ready to wear
'1637': versace,spring 2004 couture
'1638': versace,spring 2004 ready to wear
'1639': versace,spring 2005 menswear
'1640': versace,spring 2005 ready to wear
'1641': versace,spring 2006 menswear
'1642': versace,spring 2006 ready to wear
'1643': versace,spring 2007 menswear
'1644': versace,spring 2007 ready to wear
'1645': versace,spring 2008 couture
'1646': versace,spring 2008 menswear
'1647': versace,spring 2008 ready to wear
'1648': versace,spring 2009 menswear
'1649': versace,spring 2009 ready to wear
'1650': versace,spring 2010 ready to wear
'1651': versace,spring 2011 menswear
'1652': versace,spring 2011 ready to wear
'1653': versace,spring 2012 menswear
'1654': versace,spring 2012 ready to wear
'1655': versace,spring 2013 menswear
'1656': versace,spring 2013 ready to wear
'1657': versace,spring 2014 menswear
'1658': versace,spring 2014 ready to wear
'1659': versace,spring 2015 menswear
'1660': versace,spring 2015 ready to wear
'1661': versace,spring 2016 menswear
'1662': versace,spring 2016 ready to wear
'1663': versace,spring 2017 menswear
'1664': versace,spring 2017 ready to wear
'1665': versace,spring 2018 menswear
'1666': versace,spring 2018 ready to wear
'1667': versace,spring 2019 menswear
'1668': versace,spring 2019 ready to wear
'1669': versace,spring 2020 menswear
'1670': versace,spring 2020 ready to wear
'1671': versace,spring 2021 menswear
'1672': versace,spring 2021 ready to wear
'1673': versace,spring 2022 ready to wear
'1674': versace,spring 2023 menswear
'1675': versace,spring 2023 ready to wear
'1676': versace,spring 2024 ready to wear
splits:
- name: train
num_bytes: 2097138827.181
num_examples: 87547
download_size: 2042963572
dataset_size: 2097138827.181
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# vogue-runway-top15-512px
[Vogue Runway](https://www.vogue.com/fashion-shows)
- 15 fashion houses
- 1679 collections
- 87,547 images
Fashion Houses: Alexander McQueen, Armani, Balenciaga, Calvin Klein, Chanel, Dior, Fendi, Gucci, Hermes, Louis Vuitton, Prada, Ralph Lauren, Saint Laurent, Valentino, Versace.
Images are maximum height 512 pixels.
|
eswardivi/Telugu_InstructData | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 82739156
num_examples: 33350
download_size: 32498948
dataset_size: 82739156
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
language:
- te
pretty_name: I
license: mit
task_categories:
- text-generation
size_categories:
- 10K<n<100K
---
This dataset is a translated version of three original datasets, namely [HuggingFaceH4/no_robots](https://huggingface.co/datasets/HuggingFaceH4/no_robots), [databricks/databricks-dolly-15k](https://huggingface.co/datasets/databricks/dolly-15k), and a subset of Telugu from [CohereForAI/aya_dataset](https://huggingface.co/datasets/CohereForAI/aya_dataset). It has been curated and processed to create a multilingual avatar dataset. |
rkdeva/Dermnet-Test-4 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype: string
- name: class
dtype: int64
splits:
- name: train
num_bytes: 376800274.178
num_examples: 3937
download_size: 370157597
dataset_size: 376800274.178
---
# Dataset Card for "Dermnet-Test-4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
fathyshalab/massive_weather-de | ---
dataset_info:
features:
- name: id
dtype: string
- name: label
dtype: int64
- name: text
dtype: string
splits:
- name: train
num_bytes: 31902
num_examples: 573
- name: validation
num_bytes: 7264
num_examples: 126
- name: test
num_bytes: 8886
num_examples: 156
download_size: 25436
dataset_size: 48052
---
# Dataset Card for "massive_weather-de"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
treezy254/hug_stack | ---
dataset_info:
features:
- name: text
dtype: string
- name: id
dtype: string
- name: metadata
struct:
- name: file_path
dtype: string
- name: repo_id
dtype: string
- name: token_count
dtype: int64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 13359739
num_examples: 1388
download_size: 3409561
dataset_size: 13359739
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "hug_stack"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
5CD-AI/Vietnamese-mabryCodes-tiny-cot-alpaca-gg-translated | ---
task_categories:
- question-answering
language:
- en
- vi
size_categories:
- 100K<n<1M
--- |
Francesco/insects-mytwu | ---
dataset_info:
features:
- name: image_id
dtype: int64
- name: image
dtype: image
- name: width
dtype: int32
- name: height
dtype: int32
- name: objects
sequence:
- name: id
dtype: int64
- name: area
dtype: int64
- name: bbox
sequence: float32
length: 4
- name: category
dtype:
class_label:
names:
'0': insects
'1': army worm
'2': legume blister beetle
'3': red spider
'4': rice gall midge
'5': rice leaf roller
'6': rice leafhopper
'7': rice water weevil
'8': wheat phloeothrips
'9': white backed plant hopper
'10': yellow rice borer
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- cc
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- object-detection
task_ids: []
pretty_name: insects-mytwu
tags:
- rf100
---
# Dataset Card for insects-mytwu
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/insects-mytwu
- **Point of Contact:** francesco.zuppichini@gmail.com
### Dataset Summary
insects-mytwu
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/insects-mytwu
### Citation Information
```
@misc{ insects-mytwu,
title = { insects mytwu Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/insects-mytwu } },
url = { https://universe.roboflow.com/object-detection/insects-mytwu },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. |
axprok/SovietFilmTitles | ---
pretty_name: sovfilmtitles
size_categories:
- 1K<n<10K
--- |
mask-distilled-one-sec-cv12/chunk_265 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 948334080
num_examples: 186240
download_size: 966690890
dataset_size: 948334080
---
# Dataset Card for "chunk_265"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_llm-agents__tora-code-34b-v1.0 | ---
pretty_name: Evaluation run of llm-agents/tora-code-34b-v1.0
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [llm-agents/tora-code-34b-v1.0](https://huggingface.co/llm-agents/tora-code-34b-v1.0)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 3 run(s). Each run can be\
\ found as a specific 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_llm-agents__tora-code-34b-v1.0\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-01-04T21:49:30.893930](https://huggingface.co/datasets/open-llm-leaderboard/details_llm-agents__tora-code-34b-v1.0/blob/main/results_2024-01-04T21-49-30.893930.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\": 0.46739279777981946,\n\
\ \"acc_stderr\": 0.034410091993572804,\n \"acc_norm\": 0.4720435067546776,\n\
\ \"acc_norm_stderr\": 0.03515712357620925,\n \"mc1\": 0.26193390452876375,\n\
\ \"mc1_stderr\": 0.015392118805015034,\n \"mc2\": 0.39617813653906875,\n\
\ \"mc2_stderr\": 0.01499031262059852\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.47440273037542663,\n \"acc_stderr\": 0.014592230885298962,\n\
\ \"acc_norm\": 0.5025597269624573,\n \"acc_norm_stderr\": 0.014611199329843784\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5683130850428202,\n\
\ \"acc_stderr\": 0.0049429906231311166,\n \"acc_norm\": 0.754829715196176,\n\
\ \"acc_norm_stderr\": 0.004293089342105424\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.27,\n \"acc_stderr\": 0.0446196043338474,\n \
\ \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.0446196043338474\n },\n\
\ \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.3851851851851852,\n\
\ \"acc_stderr\": 0.042039210401562783,\n \"acc_norm\": 0.3851851851851852,\n\
\ \"acc_norm_stderr\": 0.042039210401562783\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.46710526315789475,\n \"acc_stderr\": 0.040601270352363966,\n\
\ \"acc_norm\": 0.46710526315789475,\n \"acc_norm_stderr\": 0.040601270352363966\n\
\ },\n \"harness|hendrycksTest-business_ethics|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-clinical_knowledge|5\"\
: {\n \"acc\": 0.4867924528301887,\n \"acc_stderr\": 0.030762134874500482,\n\
\ \"acc_norm\": 0.4867924528301887,\n \"acc_norm_stderr\": 0.030762134874500482\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.4583333333333333,\n\
\ \"acc_stderr\": 0.04166666666666665,\n \"acc_norm\": 0.4583333333333333,\n\
\ \"acc_norm_stderr\": 0.04166666666666665\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.27,\n \"acc_stderr\": 0.044619604333847394,\n \
\ \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.044619604333847394\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.42,\n \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\"\
: 0.42,\n \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness|hendrycksTest-college_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\": 0.4046242774566474,\n\
\ \"acc_stderr\": 0.03742461193887248,\n \"acc_norm\": 0.4046242774566474,\n\
\ \"acc_norm_stderr\": 0.03742461193887248\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.2647058823529412,\n \"acc_stderr\": 0.04389869956808778,\n\
\ \"acc_norm\": 0.2647058823529412,\n \"acc_norm_stderr\": 0.04389869956808778\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.69,\n \"acc_stderr\": 0.046482319871173156,\n \"acc_norm\": 0.69,\n\
\ \"acc_norm_stderr\": 0.046482319871173156\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.3872340425531915,\n \"acc_stderr\": 0.03184389265339525,\n\
\ \"acc_norm\": 0.3872340425531915,\n \"acc_norm_stderr\": 0.03184389265339525\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.35964912280701755,\n\
\ \"acc_stderr\": 0.04514496132873633,\n \"acc_norm\": 0.35964912280701755,\n\
\ \"acc_norm_stderr\": 0.04514496132873633\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.46206896551724136,\n \"acc_stderr\": 0.041546596717075474,\n\
\ \"acc_norm\": 0.46206896551724136,\n \"acc_norm_stderr\": 0.041546596717075474\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.3333333333333333,\n \"acc_stderr\": 0.024278568024307706,\n \"\
acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.024278568024307706\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.31746031746031744,\n\
\ \"acc_stderr\": 0.04163453031302859,\n \"acc_norm\": 0.31746031746031744,\n\
\ \"acc_norm_stderr\": 0.04163453031302859\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.26,\n \"acc_stderr\": 0.0440844002276808,\n \
\ \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.0440844002276808\n },\n\
\ \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.5,\n\
\ \"acc_stderr\": 0.028444006199428714,\n \"acc_norm\": 0.5,\n \
\ \"acc_norm_stderr\": 0.028444006199428714\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.35467980295566504,\n \"acc_stderr\": 0.03366124489051448,\n\
\ \"acc_norm\": 0.35467980295566504,\n \"acc_norm_stderr\": 0.03366124489051448\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.6,\n \"acc_stderr\": 0.04923659639173309,\n \"acc_norm\"\
: 0.6,\n \"acc_norm_stderr\": 0.04923659639173309\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.5757575757575758,\n \"acc_stderr\": 0.03859268142070264,\n\
\ \"acc_norm\": 0.5757575757575758,\n \"acc_norm_stderr\": 0.03859268142070264\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.5707070707070707,\n \"acc_stderr\": 0.03526552724601199,\n \"\
acc_norm\": 0.5707070707070707,\n \"acc_norm_stderr\": 0.03526552724601199\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.616580310880829,\n \"acc_stderr\": 0.03508984236295342,\n\
\ \"acc_norm\": 0.616580310880829,\n \"acc_norm_stderr\": 0.03508984236295342\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.37435897435897436,\n \"acc_stderr\": 0.024537591572830513,\n\
\ \"acc_norm\": 0.37435897435897436,\n \"acc_norm_stderr\": 0.024537591572830513\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.32222222222222224,\n \"acc_stderr\": 0.0284934650910286,\n \
\ \"acc_norm\": 0.32222222222222224,\n \"acc_norm_stderr\": 0.0284934650910286\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.42016806722689076,\n \"acc_stderr\": 0.03206183783236152,\n\
\ \"acc_norm\": 0.42016806722689076,\n \"acc_norm_stderr\": 0.03206183783236152\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.33774834437086093,\n \"acc_stderr\": 0.03861557546255169,\n \"\
acc_norm\": 0.33774834437086093,\n \"acc_norm_stderr\": 0.03861557546255169\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.6403669724770642,\n \"acc_stderr\": 0.020575234660123776,\n \"\
acc_norm\": 0.6403669724770642,\n \"acc_norm_stderr\": 0.020575234660123776\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\"\
: 0.6029411764705882,\n \"acc_stderr\": 0.0343413116471913,\n \"acc_norm\"\
: 0.6029411764705882,\n \"acc_norm_stderr\": 0.0343413116471913\n },\n\
\ \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\":\
\ 0.620253164556962,\n \"acc_stderr\": 0.031591887529658504,\n \"\
acc_norm\": 0.620253164556962,\n \"acc_norm_stderr\": 0.031591887529658504\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.4663677130044843,\n\
\ \"acc_stderr\": 0.033481800170603065,\n \"acc_norm\": 0.4663677130044843,\n\
\ \"acc_norm_stderr\": 0.033481800170603065\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.4961832061068702,\n \"acc_stderr\": 0.043851623256015534,\n\
\ \"acc_norm\": 0.4961832061068702,\n \"acc_norm_stderr\": 0.043851623256015534\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.6694214876033058,\n \"acc_stderr\": 0.04294340845212093,\n \"\
acc_norm\": 0.6694214876033058,\n \"acc_norm_stderr\": 0.04294340845212093\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.5462962962962963,\n\
\ \"acc_stderr\": 0.04812917324536823,\n \"acc_norm\": 0.5462962962962963,\n\
\ \"acc_norm_stderr\": 0.04812917324536823\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.5705521472392638,\n \"acc_stderr\": 0.03889066619112723,\n\
\ \"acc_norm\": 0.5705521472392638,\n \"acc_norm_stderr\": 0.03889066619112723\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.35714285714285715,\n\
\ \"acc_stderr\": 0.04547960999764376,\n \"acc_norm\": 0.35714285714285715,\n\
\ \"acc_norm_stderr\": 0.04547960999764376\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7087378640776699,\n \"acc_stderr\": 0.044986763205729224,\n\
\ \"acc_norm\": 0.7087378640776699,\n \"acc_norm_stderr\": 0.044986763205729224\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.717948717948718,\n\
\ \"acc_stderr\": 0.02948036054954119,\n \"acc_norm\": 0.717948717948718,\n\
\ \"acc_norm_stderr\": 0.02948036054954119\n },\n \"harness|hendrycksTest-medical_genetics|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-miscellaneous|5\": {\n \"acc\": 0.5938697318007663,\n\
\ \"acc_stderr\": 0.01756203740647892,\n \"acc_norm\": 0.5938697318007663,\n\
\ \"acc_norm_stderr\": 0.01756203740647892\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.5057803468208093,\n \"acc_stderr\": 0.026917296179149116,\n\
\ \"acc_norm\": 0.5057803468208093,\n \"acc_norm_stderr\": 0.026917296179149116\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.24022346368715083,\n\
\ \"acc_stderr\": 0.01428834380392531,\n \"acc_norm\": 0.24022346368715083,\n\
\ \"acc_norm_stderr\": 0.01428834380392531\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.4869281045751634,\n \"acc_stderr\": 0.028620130800700246,\n\
\ \"acc_norm\": 0.4869281045751634,\n \"acc_norm_stderr\": 0.028620130800700246\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.5112540192926045,\n\
\ \"acc_stderr\": 0.028390897396863533,\n \"acc_norm\": 0.5112540192926045,\n\
\ \"acc_norm_stderr\": 0.028390897396863533\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.4845679012345679,\n \"acc_stderr\": 0.02780749004427621,\n\
\ \"acc_norm\": 0.4845679012345679,\n \"acc_norm_stderr\": 0.02780749004427621\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.3404255319148936,\n \"acc_stderr\": 0.028267657482650147,\n \
\ \"acc_norm\": 0.3404255319148936,\n \"acc_norm_stderr\": 0.028267657482650147\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.34876140808344197,\n\
\ \"acc_stderr\": 0.012172035157127118,\n \"acc_norm\": 0.34876140808344197,\n\
\ \"acc_norm_stderr\": 0.012172035157127118\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.27941176470588236,\n \"acc_stderr\": 0.027257202606114948,\n\
\ \"acc_norm\": 0.27941176470588236,\n \"acc_norm_stderr\": 0.027257202606114948\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.4199346405228758,\n \"acc_stderr\": 0.019966811178256483,\n \
\ \"acc_norm\": 0.4199346405228758,\n \"acc_norm_stderr\": 0.019966811178256483\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\": 0.5959183673469388,\n \"acc_stderr\": 0.031414708025865885,\n\
\ \"acc_norm\": 0.5959183673469388,\n \"acc_norm_stderr\": 0.031414708025865885\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.5870646766169154,\n\
\ \"acc_stderr\": 0.03481520803367348,\n \"acc_norm\": 0.5870646766169154,\n\
\ \"acc_norm_stderr\": 0.03481520803367348\n },\n \"harness|hendrycksTest-us_foreign_policy|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-virology|5\": {\n \"acc\": 0.40963855421686746,\n\
\ \"acc_stderr\": 0.03828401115079021,\n \"acc_norm\": 0.40963855421686746,\n\
\ \"acc_norm_stderr\": 0.03828401115079021\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.6140350877192983,\n \"acc_stderr\": 0.03733756969066164,\n\
\ \"acc_norm\": 0.6140350877192983,\n \"acc_norm_stderr\": 0.03733756969066164\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.26193390452876375,\n\
\ \"mc1_stderr\": 0.015392118805015034,\n \"mc2\": 0.39617813653906875,\n\
\ \"mc2_stderr\": 0.01499031262059852\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.6771902131018153,\n \"acc_stderr\": 0.013140498173357943\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.19787717968157695,\n \
\ \"acc_stderr\": 0.010973889601756328\n }\n}\n```"
repo_url: https://huggingface.co/llm-agents/tora-code-34b-v1.0
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_10_10T19_58_46.874384
path:
- '**/details_harness|arc:challenge|25_2023-10-10T19-58-46.874384.parquet'
- split: 2024_01_04T21_49_30.893930
path:
- '**/details_harness|arc:challenge|25_2024-01-04T21-49-30.893930.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-01-04T21-49-30.893930.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_29T14_45_33.469419
path:
- '**/details_harness|drop|3_2023-10-29T14-45-33.469419.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-29T14-45-33.469419.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_29T14_45_33.469419
path:
- '**/details_harness|gsm8k|5_2023-10-29T14-45-33.469419.parquet'
- split: 2024_01_04T21_49_30.893930
path:
- '**/details_harness|gsm8k|5_2024-01-04T21-49-30.893930.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-01-04T21-49-30.893930.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_10_10T19_58_46.874384
path:
- '**/details_harness|hellaswag|10_2023-10-10T19-58-46.874384.parquet'
- split: 2024_01_04T21_49_30.893930
path:
- '**/details_harness|hellaswag|10_2024-01-04T21-49-30.893930.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-01-04T21-49-30.893930.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_10_10T19_58_46.874384
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-10T19-58-46.874384.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-10T19-58-46.874384.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-10T19-58-46.874384.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-10T19-58-46.874384.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-10T19-58-46.874384.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-10T19-58-46.874384.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-10T19-58-46.874384.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-10T19-58-46.874384.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-10T19-58-46.874384.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-10T19-58-46.874384.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-10T19-58-46.874384.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-10T19-58-46.874384.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-10T19-58-46.874384.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-10T19-58-46.874384.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-10T19-58-46.874384.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-10T19-58-46.874384.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-10T19-58-46.874384.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-10T19-58-46.874384.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-10T19-58-46.874384.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-10T19-58-46.874384.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-10T19-58-46.874384.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-10T19-58-46.874384.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-10T19-58-46.874384.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-10T19-58-46.874384.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-10T19-58-46.874384.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-10T19-58-46.874384.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-10T19-58-46.874384.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-10T19-58-46.874384.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-10T19-58-46.874384.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-10T19-58-46.874384.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-10T19-58-46.874384.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-10T19-58-46.874384.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-10T19-58-46.874384.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-10T19-58-46.874384.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-10-10T19-58-46.874384.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-10T19-58-46.874384.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-10T19-58-46.874384.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-10T19-58-46.874384.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-10-10T19-58-46.874384.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-10-10T19-58-46.874384.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-10T19-58-46.874384.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-10T19-58-46.874384.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-10T19-58-46.874384.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-10T19-58-46.874384.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-10T19-58-46.874384.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-10T19-58-46.874384.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-10T19-58-46.874384.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-10T19-58-46.874384.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-10T19-58-46.874384.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-10T19-58-46.874384.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-10T19-58-46.874384.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-10T19-58-46.874384.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-10T19-58-46.874384.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-10T19-58-46.874384.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-10T19-58-46.874384.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-10T19-58-46.874384.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-10T19-58-46.874384.parquet'
- split: 2024_01_04T21_49_30.893930
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-04T21-49-30.893930.parquet'
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- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-04T21-49-30.893930.parquet'
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- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-04T21-49-30.893930.parquet'
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- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-04T21-49-30.893930.parquet'
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- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-04T21-49-30.893930.parquet'
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- '**/details_harness|hendrycksTest-security_studies|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-04T21-49-30.893930.parquet'
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- '**/details_harness|hendrycksTest-virology|5_2024-01-04T21-49-30.893930.parquet'
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- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-04T21-49-30.893930.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-04T21-49-30.893930.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_10_10T19_58_46.874384
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-10T19-58-46.874384.parquet'
- split: 2024_01_04T21_49_30.893930
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-04T21-49-30.893930.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-04T21-49-30.893930.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_10_10T19_58_46.874384
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-10T19-58-46.874384.parquet'
- split: 2024_01_04T21_49_30.893930
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-04T21-49-30.893930.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-04T21-49-30.893930.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_10_10T19_58_46.874384
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-10T19-58-46.874384.parquet'
- split: 2024_01_04T21_49_30.893930
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-04T21-49-30.893930.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-04T21-49-30.893930.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_10_10T19_58_46.874384
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-10T19-58-46.874384.parquet'
- split: 2024_01_04T21_49_30.893930
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-04T21-49-30.893930.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-04T21-49-30.893930.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_10_10T19_58_46.874384
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-10T19-58-46.874384.parquet'
- split: 2024_01_04T21_49_30.893930
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-04T21-49-30.893930.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-04T21-49-30.893930.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_10_10T19_58_46.874384
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-10T19-58-46.874384.parquet'
- split: 2024_01_04T21_49_30.893930
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-04T21-49-30.893930.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-04T21-49-30.893930.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_10_10T19_58_46.874384
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-10T19-58-46.874384.parquet'
- split: 2024_01_04T21_49_30.893930
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-04T21-49-30.893930.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-04T21-49-30.893930.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_10_10T19_58_46.874384
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-10T19-58-46.874384.parquet'
- split: 2024_01_04T21_49_30.893930
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-04T21-49-30.893930.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-04T21-49-30.893930.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-10T19-58-46.874384.parquet'
- split: 2024_01_04T21_49_30.893930
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-04T21-49-30.893930.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-04T21-49-30.893930.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
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path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-10T19-58-46.874384.parquet'
- split: 2024_01_04T21_49_30.893930
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-04T21-49-30.893930.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-04T21-49-30.893930.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-10T19-58-46.874384.parquet'
- split: 2024_01_04T21_49_30.893930
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-04T21-49-30.893930.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-04T21-49-30.893930.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
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path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-10T19-58-46.874384.parquet'
- split: 2024_01_04T21_49_30.893930
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-04T21-49-30.893930.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-04T21-49-30.893930.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_10_10T19_58_46.874384
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-10T19-58-46.874384.parquet'
- split: 2024_01_04T21_49_30.893930
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-04T21-49-30.893930.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-04T21-49-30.893930.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-10T19-58-46.874384.parquet'
- split: 2024_01_04T21_49_30.893930
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-04T21-49-30.893930.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-04T21-49-30.893930.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
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path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-10T19-58-46.874384.parquet'
- split: 2024_01_04T21_49_30.893930
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-04T21-49-30.893930.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-04T21-49-30.893930.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-10T19-58-46.874384.parquet'
- split: 2024_01_04T21_49_30.893930
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-04T21-49-30.893930.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-04T21-49-30.893930.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
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path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-10T19-58-46.874384.parquet'
- split: 2024_01_04T21_49_30.893930
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-04T21-49-30.893930.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-04T21-49-30.893930.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
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path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-10T19-58-46.874384.parquet'
- split: 2024_01_04T21_49_30.893930
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-04T21-49-30.893930.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-04T21-49-30.893930.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-10T19-58-46.874384.parquet'
- split: 2024_01_04T21_49_30.893930
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-04T21-49-30.893930.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-04T21-49-30.893930.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-10T19-58-46.874384.parquet'
- split: 2024_01_04T21_49_30.893930
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-04T21-49-30.893930.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-04T21-49-30.893930.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-10T19-58-46.874384.parquet'
- split: 2024_01_04T21_49_30.893930
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-04T21-49-30.893930.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-04T21-49-30.893930.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_10_10T19_58_46.874384
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-10T19-58-46.874384.parquet'
- split: 2024_01_04T21_49_30.893930
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-04T21-49-30.893930.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-04T21-49-30.893930.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_10_10T19_58_46.874384
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-10T19-58-46.874384.parquet'
- split: 2024_01_04T21_49_30.893930
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-04T21-49-30.893930.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-04T21-49-30.893930.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_10_10T19_58_46.874384
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-10T19-58-46.874384.parquet'
- split: 2024_01_04T21_49_30.893930
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-04T21-49-30.893930.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-04T21-49-30.893930.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_10_10T19_58_46.874384
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-10T19-58-46.874384.parquet'
- split: 2024_01_04T21_49_30.893930
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-04T21-49-30.893930.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-04T21-49-30.893930.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_10_10T19_58_46.874384
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-10T19-58-46.874384.parquet'
- split: 2024_01_04T21_49_30.893930
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-04T21-49-30.893930.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-04T21-49-30.893930.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_10_10T19_58_46.874384
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-10T19-58-46.874384.parquet'
- split: 2024_01_04T21_49_30.893930
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-04T21-49-30.893930.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-04T21-49-30.893930.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_10_10T19_58_46.874384
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-10T19-58-46.874384.parquet'
- split: 2024_01_04T21_49_30.893930
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-04T21-49-30.893930.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-04T21-49-30.893930.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_10_10T19_58_46.874384
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-10T19-58-46.874384.parquet'
- split: 2024_01_04T21_49_30.893930
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-04T21-49-30.893930.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-04T21-49-30.893930.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_10_10T19_58_46.874384
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-10T19-58-46.874384.parquet'
- split: 2024_01_04T21_49_30.893930
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-04T21-49-30.893930.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-04T21-49-30.893930.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_10_10T19_58_46.874384
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-10T19-58-46.874384.parquet'
- split: 2024_01_04T21_49_30.893930
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-04T21-49-30.893930.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-04T21-49-30.893930.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_10_10T19_58_46.874384
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-10T19-58-46.874384.parquet'
- split: 2024_01_04T21_49_30.893930
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-04T21-49-30.893930.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-04T21-49-30.893930.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_10_10T19_58_46.874384
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-10T19-58-46.874384.parquet'
- split: 2024_01_04T21_49_30.893930
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-04T21-49-30.893930.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-04T21-49-30.893930.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_10_10T19_58_46.874384
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-10T19-58-46.874384.parquet'
- split: 2024_01_04T21_49_30.893930
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-04T21-49-30.893930.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-04T21-49-30.893930.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_10_10T19_58_46.874384
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-10T19-58-46.874384.parquet'
- split: 2024_01_04T21_49_30.893930
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-04T21-49-30.893930.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-04T21-49-30.893930.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_10_10T19_58_46.874384
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-10T19-58-46.874384.parquet'
- split: 2024_01_04T21_49_30.893930
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-04T21-49-30.893930.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-04T21-49-30.893930.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_10_10T19_58_46.874384
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-10T19-58-46.874384.parquet'
- split: 2024_01_04T21_49_30.893930
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-04T21-49-30.893930.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-04T21-49-30.893930.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_10_10T19_58_46.874384
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-10T19-58-46.874384.parquet'
- split: 2024_01_04T21_49_30.893930
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-04T21-49-30.893930.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-04T21-49-30.893930.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_10_10T19_58_46.874384
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-10T19-58-46.874384.parquet'
- split: 2024_01_04T21_49_30.893930
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-04T21-49-30.893930.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-04T21-49-30.893930.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_10_10T19_58_46.874384
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-10T19-58-46.874384.parquet'
- split: 2024_01_04T21_49_30.893930
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-04T21-49-30.893930.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-04T21-49-30.893930.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_10_10T19_58_46.874384
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-10T19-58-46.874384.parquet'
- split: 2024_01_04T21_49_30.893930
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-04T21-49-30.893930.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-04T21-49-30.893930.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_10_10T19_58_46.874384
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-10T19-58-46.874384.parquet'
- split: 2024_01_04T21_49_30.893930
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-04T21-49-30.893930.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-04T21-49-30.893930.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_10_10T19_58_46.874384
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-10T19-58-46.874384.parquet'
- split: 2024_01_04T21_49_30.893930
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-04T21-49-30.893930.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-04T21-49-30.893930.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_10_10T19_58_46.874384
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-10T19-58-46.874384.parquet'
- split: 2024_01_04T21_49_30.893930
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-04T21-49-30.893930.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-04T21-49-30.893930.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_10_10T19_58_46.874384
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-10T19-58-46.874384.parquet'
- split: 2024_01_04T21_49_30.893930
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-04T21-49-30.893930.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-04T21-49-30.893930.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_10_10T19_58_46.874384
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-10T19-58-46.874384.parquet'
- split: 2024_01_04T21_49_30.893930
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-04T21-49-30.893930.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-04T21-49-30.893930.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_10_10T19_58_46.874384
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-10T19-58-46.874384.parquet'
- split: 2024_01_04T21_49_30.893930
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-04T21-49-30.893930.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-04T21-49-30.893930.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_10_10T19_58_46.874384
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-10T19-58-46.874384.parquet'
- split: 2024_01_04T21_49_30.893930
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-04T21-49-30.893930.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-04T21-49-30.893930.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_10_10T19_58_46.874384
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-10T19-58-46.874384.parquet'
- split: 2024_01_04T21_49_30.893930
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-04T21-49-30.893930.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-04T21-49-30.893930.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_10_10T19_58_46.874384
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-10T19-58-46.874384.parquet'
- split: 2024_01_04T21_49_30.893930
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-04T21-49-30.893930.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-04T21-49-30.893930.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_10_10T19_58_46.874384
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-10T19-58-46.874384.parquet'
- split: 2024_01_04T21_49_30.893930
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-04T21-49-30.893930.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-04T21-49-30.893930.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_10_10T19_58_46.874384
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-10T19-58-46.874384.parquet'
- split: 2024_01_04T21_49_30.893930
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-04T21-49-30.893930.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-04T21-49-30.893930.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_10_10T19_58_46.874384
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-10T19-58-46.874384.parquet'
- split: 2024_01_04T21_49_30.893930
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-04T21-49-30.893930.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-04T21-49-30.893930.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_10_10T19_58_46.874384
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-10T19-58-46.874384.parquet'
- split: 2024_01_04T21_49_30.893930
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-04T21-49-30.893930.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-04T21-49-30.893930.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_10_10T19_58_46.874384
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-10T19-58-46.874384.parquet'
- split: 2024_01_04T21_49_30.893930
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-04T21-49-30.893930.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-04T21-49-30.893930.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_10_10T19_58_46.874384
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-10T19-58-46.874384.parquet'
- split: 2024_01_04T21_49_30.893930
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-04T21-49-30.893930.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-04T21-49-30.893930.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_10_10T19_58_46.874384
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-10T19-58-46.874384.parquet'
- split: 2024_01_04T21_49_30.893930
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-04T21-49-30.893930.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-04T21-49-30.893930.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_10_10T19_58_46.874384
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-10T19-58-46.874384.parquet'
- split: 2024_01_04T21_49_30.893930
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-04T21-49-30.893930.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-04T21-49-30.893930.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_29T14_45_33.469419
path:
- '**/details_harness|winogrande|5_2023-10-29T14-45-33.469419.parquet'
- split: 2024_01_04T21_49_30.893930
path:
- '**/details_harness|winogrande|5_2024-01-04T21-49-30.893930.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-01-04T21-49-30.893930.parquet'
- config_name: results
data_files:
- split: 2023_10_10T19_58_46.874384
path:
- results_2023-10-10T19-58-46.874384.parquet
- split: 2023_10_29T14_45_33.469419
path:
- results_2023-10-29T14-45-33.469419.parquet
- split: 2024_01_04T21_49_30.893930
path:
- results_2024-01-04T21-49-30.893930.parquet
- split: latest
path:
- results_2024-01-04T21-49-30.893930.parquet
---
# Dataset Card for Evaluation run of llm-agents/tora-code-34b-v1.0
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [llm-agents/tora-code-34b-v1.0](https://huggingface.co/llm-agents/tora-code-34b-v1.0) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 3 run(s). Each run can be found as a specific 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_llm-agents__tora-code-34b-v1.0",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-04T21:49:30.893930](https://huggingface.co/datasets/open-llm-leaderboard/details_llm-agents__tora-code-34b-v1.0/blob/main/results_2024-01-04T21-49-30.893930.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": 0.46739279777981946,
"acc_stderr": 0.034410091993572804,
"acc_norm": 0.4720435067546776,
"acc_norm_stderr": 0.03515712357620925,
"mc1": 0.26193390452876375,
"mc1_stderr": 0.015392118805015034,
"mc2": 0.39617813653906875,
"mc2_stderr": 0.01499031262059852
},
"harness|arc:challenge|25": {
"acc": 0.47440273037542663,
"acc_stderr": 0.014592230885298962,
"acc_norm": 0.5025597269624573,
"acc_norm_stderr": 0.014611199329843784
},
"harness|hellaswag|10": {
"acc": 0.5683130850428202,
"acc_stderr": 0.0049429906231311166,
"acc_norm": 0.754829715196176,
"acc_norm_stderr": 0.004293089342105424
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.27,
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"acc_norm": 0.27,
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},
"harness|hendrycksTest-anatomy|5": {
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},
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},
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"acc_norm": 0.51,
"acc_norm_stderr": 0.05024183937956911
},
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"acc_norm_stderr": 0.030762134874500482
},
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},
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},
"harness|hendrycksTest-college_computer_science|5": {
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"acc_norm": 0.42,
"acc_norm_stderr": 0.049604496374885836
},
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"acc_norm": 0.3,
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},
"harness|hendrycksTest-college_medicine|5": {
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},
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"acc_norm_stderr": 0.04389869956808778
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.69,
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"acc_norm": 0.69,
"acc_norm_stderr": 0.046482319871173156
},
"harness|hendrycksTest-conceptual_physics|5": {
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},
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},
"harness|hendrycksTest-electrical_engineering|5": {
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},
"harness|hendrycksTest-elementary_mathematics|5": {
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},
"harness|hendrycksTest-formal_logic|5": {
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},
"harness|hendrycksTest-global_facts|5": {
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"acc_norm": 0.26,
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},
"harness|hendrycksTest-high_school_biology|5": {
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},
"harness|hendrycksTest-high_school_chemistry|5": {
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},
"harness|hendrycksTest-high_school_computer_science|5": {
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},
"harness|hendrycksTest-high_school_european_history|5": {
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},
"harness|hendrycksTest-high_school_geography|5": {
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},
"harness|hendrycksTest-high_school_government_and_politics|5": {
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"acc_norm_stderr": 0.03508984236295342
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
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"acc_norm_stderr": 0.024537591572830513
},
"harness|hendrycksTest-high_school_mathematics|5": {
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"acc_norm_stderr": 0.0284934650910286
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.42016806722689076,
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"acc_norm": 0.42016806722689076,
"acc_norm_stderr": 0.03206183783236152
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.33774834437086093,
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"acc_norm": 0.33774834437086093,
"acc_norm_stderr": 0.03861557546255169
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.6403669724770642,
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"acc_norm": 0.6403669724770642,
"acc_norm_stderr": 0.020575234660123776
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.32407407407407407,
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"acc_norm": 0.32407407407407407,
"acc_norm_stderr": 0.03191923445686185
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.6029411764705882,
"acc_stderr": 0.0343413116471913,
"acc_norm": 0.6029411764705882,
"acc_norm_stderr": 0.0343413116471913
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.620253164556962,
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},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.4663677130044843,
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},
"harness|hendrycksTest-human_sexuality|5": {
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},
"harness|hendrycksTest-international_law|5": {
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},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.5462962962962963,
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"acc_norm_stderr": 0.04812917324536823
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.5705521472392638,
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},
"harness|hendrycksTest-machine_learning|5": {
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"acc_norm_stderr": 0.04547960999764376
},
"harness|hendrycksTest-management|5": {
"acc": 0.7087378640776699,
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},
"harness|hendrycksTest-marketing|5": {
"acc": 0.717948717948718,
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"acc_norm": 0.717948717948718,
"acc_norm_stderr": 0.02948036054954119
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.57,
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"acc_norm": 0.57,
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},
"harness|hendrycksTest-miscellaneous|5": {
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},
"harness|hendrycksTest-moral_disputes|5": {
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},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.24022346368715083,
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},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.4869281045751634,
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"acc_norm": 0.4869281045751634,
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},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.5112540192926045,
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"acc_norm": 0.5112540192926045,
"acc_norm_stderr": 0.028390897396863533
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.4845679012345679,
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},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.3404255319148936,
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"acc_norm": 0.3404255319148936,
"acc_norm_stderr": 0.028267657482650147
},
"harness|hendrycksTest-professional_law|5": {
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"acc_norm": 0.34876140808344197,
"acc_norm_stderr": 0.012172035157127118
},
"harness|hendrycksTest-professional_medicine|5": {
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},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.4199346405228758,
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"acc_norm_stderr": 0.019966811178256483
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.5363636363636364,
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"acc_norm_stderr": 0.04776449162396197
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.5959183673469388,
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"acc_norm": 0.5959183673469388,
"acc_norm_stderr": 0.031414708025865885
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.5870646766169154,
"acc_stderr": 0.03481520803367348,
"acc_norm": 0.5870646766169154,
"acc_norm_stderr": 0.03481520803367348
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.63,
"acc_stderr": 0.04852365870939099,
"acc_norm": 0.63,
"acc_norm_stderr": 0.04852365870939099
},
"harness|hendrycksTest-virology|5": {
"acc": 0.40963855421686746,
"acc_stderr": 0.03828401115079021,
"acc_norm": 0.40963855421686746,
"acc_norm_stderr": 0.03828401115079021
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.6140350877192983,
"acc_stderr": 0.03733756969066164,
"acc_norm": 0.6140350877192983,
"acc_norm_stderr": 0.03733756969066164
},
"harness|truthfulqa:mc|0": {
"mc1": 0.26193390452876375,
"mc1_stderr": 0.015392118805015034,
"mc2": 0.39617813653906875,
"mc2_stderr": 0.01499031262059852
},
"harness|winogrande|5": {
"acc": 0.6771902131018153,
"acc_stderr": 0.013140498173357943
},
"harness|gsm8k|5": {
"acc": 0.19787717968157695,
"acc_stderr": 0.010973889601756328
}
}
```
## 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] |
librarian-bots/dataset_cards_with_metadata | ---
size_categories:
- 10K<n<100K
task_categories:
- text-retrieval
dataset_info:
features:
- name: datasetId
dtype: string
- name: author
dtype: string
- name: last_modified
dtype: timestamp[us, tz=UTC]
- name: downloads
dtype: int64
- name: likes
dtype: int64
- name: tags
sequence: string
- name: task_categories
sequence: string
- name: createdAt
dtype: timestamp[us, tz=UTC]
- name: card
dtype: string
splits:
- name: train
num_bytes: 548524828
num_examples: 90435
download_size: 127114872
dataset_size: 548524828
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
tags:
- ethics
- documentation
---
# Dataset Card for Hugging Face Hub Dataset Cards
This datasets consists of [dataset cards](https://huggingface.co/docs/hub/datasets-cards) for models hosted on the Hugging Face Hub. The dataset cards are created by the community and provide information about datasets hosted on the Hugging Face Hub.
This dataset is updated on a daily basis and includes publicly available datasets on the Hugging Face Hub.
This dataset is made available to help support users wanting to work with a large number of Dataset Cards from the Hub. We hope that this dataset will help support research in the area of Dataset Cards and their use but the format of this dataset may not be useful for all use cases. If there are other features that you would like to see included in this dataset, please open a new [discussion](https://huggingface.co/datasets/librarian-bots/model_cards_with_metadata/discussions/new).
## Dataset Details
### Dataset Description
- **Curated by:** Daniel van Strien
- **Language(s) (NLP):** Dataset cards on the Hugging Face Hub are predominantly in English but may include other languages.
## Uses
There are a number of potential uses for this dataset including:
- text mining to find common themes in dataset cards
- analysis of the dataset card format/content
- topic modelling of dataset cards
- training language models on the dataset cards
### Out-of-Scope Use
[More Information Needed]
## Dataset Structure
This dataset has a single split.
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
The dataset was created to assist people in working with dataset cards. In particular it was created to support research in the area of dataset cards and their use. It is possible to use the Hugging Face Hub API or client library to download dataset cards and this option may be preferable if you have a very specific use case or require a different format.
### Source Data
The source data is `README.md` files for datasets hosted on the Hugging Face Hub. We do not include any other supplementary files that may be included in the dataset directory.
#### 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. -->
The data is downloaded using a CRON job on a daily basis.
#### Who are the source data producers?
The source data producers are the creators of the dataset cards on the Hugging Face Hub. This includes a broad variety of people from the community ranging from large companies to individual researchers. We do not gather any information about who created the dataset card in this repository although this information can be gathered from the Hugging Face Hub API.
### Annotations [optional]
There are no additional annotations in this dataset beyond the dataset card content.
#### Annotation process
N/A
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
N/A
#### 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. -->
We make no effort to anonymize the data. Whilst we don't expect the majority of dataset cards to contain personal or sensitive information, it is possible that some dataset cards may contain this information. Dataset cards may also link to websites or email addresses.
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
Dataset cards are created by the community and we do not have any control over the content of the dataset cards. We do not review the content of the dataset cards and we do not make any claims about the accuracy of the information in the dataset cards.
Some dataset cards will themselves discuss bias and sometimes this is done by providing examples of bias in either the training data or the responses provided by the dataset. As a result this dataset may contain examples of bias.
Whilst we do not directly download any images linked to in the dataset cards, some dataset cards may include images. Some of these images may not be suitable for all audiences.
### 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
No formal citation is required for this dataset but if you use this dataset in your work, please include a link to this dataset page.
## Dataset Card Authors
[@davanstrien](https://huggingface.co/davanstrien)
## Dataset Card Contact
[@davanstrien](https://huggingface.co/davanstrien) |
zhouruiyang/RR-MCQ | ---
license: mit
task_categories:
- question-answering
---
# Dataset Card for Dataset Name
<!-- Provide a quick summary of the dataset. -->
RR-MCQ (Review-Rebuttal Multiple-Choice Question) is an evaluation dataset for models' reviewing-related abilities.
## Dataset Details
<!-- Provide a longer summary of what this dataset is. -->
Description:
- contains 196 multiple-choice questions with 1-4 correct answers;
- questions are based on the review-rebuttal forums of ICLR-2023 on Openreview;
- each question is generated from a related argument=(review, response);
- each question has 4 types of labels: review aspect, paper content, ability, need extra info.
Content:
- paper basic info: title, keywords, tl_dr, abstract, decision;
- argument pair (review, response);
- question, 4 options, answers;
- 4 labels.
## Citation
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
Please cite the paper of LREC-COLING 2024 "Is LLM a Reliable Reviewer? A Comprehensive Evaluation of LLM on Automatic Paper Reviewing Tasks". |
CyberHarem/yuzu_iizuka_sakuratrick | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of Yuzu Iizuka
This is the dataset of Yuzu Iizuka, containing 185 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
| Name | Images | Download | Description |
|:----------------|---------:|:----------------------------------------|:-----------------------------------------------------------------------------------------|
| raw | 185 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 446 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| raw-stage3-eyes | 492 | [Download](dataset-raw-stage3-eyes.zip) | 3-stage cropped (with eye-focus) raw data with meta information. |
| 384x512 | 185 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x704 | 185 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x880 | 185 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 446 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 446 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-p512-640 | 378 | [Download](dataset-stage3-p512-640.zip) | 3-stage cropped dataset with the area not less than 512x512 pixels. |
| stage3-eyes-640 | 492 | [Download](dataset-stage3-eyes-640.zip) | 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 640 pixels. |
| stage3-eyes-800 | 492 | [Download](dataset-stage3-eyes-800.zip) | 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 800 pixels. |
|
Varun53/AI_text_detection | ---
language:
- en
size_categories:
- 1K<n<10K
dataset_info:
features:
- name: Summary
dtype: string
- name: label
dtype:
class_label:
names:
'0': human
'1': AI
splits:
- name: train
num_bytes: 192666
num_examples: 1576
- name: test
num_bytes: 82517
num_examples: 676
download_size: 146353
dataset_size: 275183
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
zkdeng/inatSpiders | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': Abba_transversa
'1': Acacesia_hamata
'2': Acalitus_brevitarsus
'3': Acalitus_ferrugineum
'4': Acalitus_longisetosus
'5': Acanthepeira_stellata
'6': Acantholycosa_lignaria
'7': Acanthopachylus_robustus
'8': Acanthophrynus_coronatus
'9': Acanthoscurria_natalensis
'10': Aceria_aloinis
'11': Aceria_baccharices
'12': Aceria_baccharipha
'13': Aceria_boycei
'14': Aceria_brachytarsa
'15': Aceria_calaceris
'16': Aceria_caliberberis
'17': Aceria_campestricola
'18': Aceria_caryae
'19': Aceria_caulis
'20': Aceria_celtis
'21': Aceria_cephalanthi
'22': Aceria_cephalonea
'23': Aceria_cinereae
'24': Aceria_dina
'25': Aceria_dispar
'26': Aceria_echii
'27': Aceria_elongata
'28': Aceria_erinea
'29': Aceria_fraxini
'30': Aceria_fraxiniflora
'31': Aceria_fraxinivora
'32': Aceria_genistae
'33': Aceria_ilicis
'34': Aceria_mackiei
'35': Aceria_macrochela
'36': Aceria_macrorhyncha
'37': Aceria_modesta
'38': Aceria_myriadeum
'39': Aceria_negundi
'40': Aceria_nyssae
'41': Aceria_paracalifornica
'42': Aceria_parapopuli
'43': Aceria_parulmi
'44': Aceria_pseudoplatani
'45': Aceria_quercerina
'46': Aceria_querci
'47': Aceria_theospyri
'48': Aceria_trichophila
'49': Aceria_trinema
'50': Aceria_triplacis
'51': Aceria_vaga
'52': Actinosoma_pentacanthum
'53': Aculepeira_armida
'54': Aculepeira_ceropegia
'55': Aculepeira_packardi
'56': Aculops_aenigma
'57': Aculops_rhois
'58': Aculus_minutissimus
'59': Aculus_tetanothrix
'60': Aegaeobuthus_gibbosus
'61': Aelurillus_dubatolovi
'62': Aelurillus_luctuosus
'63': Aelurillus_m-nigrum
'64': Aelurillus_v-insignitus
'65': Agalenatea_redii
'66': Agelena_labyrinthica
'67': Agelena_orientalis
'68': Agelenopsis_aperta
'69': Agelenopsis_potteri
'70': Aglaoctenus_castaneus
'71': Aglaoctenus_lagotis
'72': Algidia_chiltoni
'73': Algidia_nigriflava
'74': Allagelena_gracilens
'75': Allocosa_funerea
'76': Allocyclosa_bifurca
'77': Allotrochosina_schauinslandi
'78': Alopecosa_albofasciata
'79': Alopecosa_barbipes
'80': Alopecosa_cuneata
'81': Alopecosa_inquilina
'82': Alopecosa_kochi
'83': Alopecosa_pulverulenta
'84': Alpaida_acuta
'85': Alpaida_alticeps
'86': Alpaida_bicornuta
'87': Alpaida_carminea
'88': Alpaida_gallardoi
'89': Alpaida_grayi
'90': Alpaida_leucogramma
'91': Alpaida_rubellula
'92': Alpaida_truncata
'93': Alpaida_variabilis
'94': Alpaida_veniliae
'95': Alpaida_versicolor
'96': Amaurobius_erberi
'97': Amaurobius_fenestralis
'98': Amaurobius_ferox
'99': Amaurobius_similis
'100': Amblyocarenum_walckenaeri
'101': Amblyomma_americanum
'102': Amblyomma_hebraeum
'103': Amblyomma_maculatum
'104': Amblyomma_triguttatum
'105': Amilenus_aurantiacus
'106': Amyciaea_forticeps
'107': Anahita_punctulata
'108': Anarrhotus_fossulatus
'109': Anasaitis_canosa
'110': Ancylometes_bogotensis
'111': Ancylometes_concolor
'112': Ancylometes_rufus
'113': Anelosimus_eximius
'114': Anelosimus_studiosus
'115': Anelosimus_vittatus
'116': Anepsion_maritatum
'117': Anoteropsis_hilaris
'118': Anoteropsis_litoralis
'119': Antrodiaetus_pacificus
'120': Antrodiaetus_unicolor
'121': Anuroctonus_phaiodactylus
'122': Anuroctonus_pococki
'123': Anyphaena_accentuata
'124': Anyphaena_numida
'125': Aoaraneus_pentagrammicus
'126': Aphantaulax_trifasciata
'127': Aphantochilus_rogersi
'128': Aphonopelma_anax
'129': Aphonopelma_armada
'130': Aphonopelma_chalcodes
'131': Aphonopelma_crinirufum
'132': Aphonopelma_eutylenum
'133': Aphonopelma_gabeli
'134': Aphonopelma_hentzi
'135': Aphonopelma_iodius
'136': Aphonopelma_johnnycashi
'137': Aphonopelma_marxi
'138': Aphonopelma_pallidum
'139': Aphonopelma_seemanni
'140': Aphonopelma_steindachneri
'141': Aphonopelma_vorhiesi
'142': Apricia_bracteata
'143': Apricia_jovialis
'144': Arachnura_feredayi
'145': Arachnura_higginsi
'146': Arachnura_melanura
'147': Arachnura_scorpionoides
'148': Arachosia_praesignis
'149': Araneus_albotriangulus
'150': Araneus_alboventris
'151': Araneus_alsine
'152': Araneus_andrewsi
'153': Araneus_angulatus
'154': Araneus_apricus
'155': Araneus_bicentenarius
'156': Araneus_cavaticus
'157': Araneus_cingulatus
'158': Araneus_circe
'159': Araneus_circulissparsus
'160': Araneus_detrimentosus
'161': Araneus_diadematus
'162': Araneus_ejusmodi
'163': Araneus_gemma
'164': Araneus_gemmoides
'165': Araneus_granadensis
'166': Araneus_grossus
'167': Araneus_guttatus
'168': Araneus_guttulatus
'169': Araneus_lathyrinus
'170': Araneus_marmoreus
'171': Araneus_miniatus
'172': Araneus_nordmanni
'173': Araneus_pallidus
'174': Araneus_pegnia
'175': Araneus_pratensis
'176': Araneus_quadratus
'177': Araneus_rotundulus
'178': Araneus_saevus
'179': Araneus_sturmi
'180': Araneus_talipedatus
'181': Araneus_thaddeus
'182': Araneus_trifolium
'183': Araneus_triguttatus
'184': Araneus_uniformis
'185': Araneus_venatrix
'186': Araneus_ventricosus
'187': Araneus_viridiventris
'188': Araneus_workmani
'189': Araniella_alpica
'190': Araniella_cucurbitina
'191': Araniella_displicata
'192': Araniella_opisthographa
'193': Arasia_mollicoma
'194': Architis_spinipes
'195': Arctosa_cinerea
'196': Arctosa_leopardus
'197': Arctosa_littoralis
'198': Arctosa_perita
'199': Arctosa_personata
'200': Argiope_aemula
'201': Argiope_aetherea
'202': Argiope_aetheroides
'203': Argiope_amoena
'204': Argiope_anasuja
'205': Argiope_appensa
'206': Argiope_argentata
'207': Argiope_aurantia
'208': Argiope_australis
'209': Argiope_bruennichi
'210': Argiope_catenulata
'211': Argiope_dang
'212': Argiope_florida
'213': Argiope_keyserlingi
'214': Argiope_lobata
'215': Argiope_magnifica
'216': Argiope_mascordi
'217': Argiope_minuta
'218': Argiope_ocula
'219': Argiope_ocyaloides
'220': Argiope_perforata
'221': Argiope_picta
'222': Argiope_protensa
'223': Argiope_radon
'224': Argiope_reinwardti
'225': Argiope_submaronica
'226': Argiope_trifasciata
'227': Argiope_versicolor
'228': Argyrodes_antipodianus
'229': Argyrodes_argyrodes
'230': Argyrodes_elevatus
'231': Argyrodes_flavescens
'232': Argyrodes_miniaceus
'233': Argyroneta_aquatica
'234': Ariadna_bicolor
'235': Ariamnes_colubrinus
'236': Ariamnes_cylindrogaster
'237': Arkys_alatus
'238': Arkys_alticephala
'239': Arkys_cornutus
'240': Arkys_curtulus
'241': Arkys_dilatatus
'242': Arkys_furcatus
'243': Arkys_lancearius
'244': Arkys_speechleyi
'245': Arkys_tuberculatus
'246': Arkys_walckenaeri
'247': Artabrus_erythrocephalus
'248': Artema_atlanta
'249': Asagena_americana
'250': Asagena_phalerata
'251': Asaphobelis_physonychus
'252': Asemonea_tenuipes
'253': Asianellus_festivus
'254': Asianopis_aurita
'255': Asianopis_subrufa
'256': Asthenoctenus_borellii
'257': Astia_hariola
'258': Astilodes_mariae
'259': Attinella_concolor
'260': Attinella_dorsata
'261': Attulus_ammophilus
'262': Attulus_avocator
'263': Attulus_fasciger
'264': Attulus_floricola
'265': Attulus_mirandus
'266': Attulus_monstrabilis
'267': Attulus_pubescens
'268': Attulus_terebratus
'269': Attulus_zimmermanni
'270': Atypoides_riversi
'271': Atypus_affinis
'272': Aulonia_albimana
'273': Austracantha_minax
'274': Australomimetus_hartleyensis
'275': Australomisidia_pilula
'276': Avicularia_avicularia
'277': Avicularia_juruensis
'278': Avicularia_purpurea
'279': Avicularia_rufa
'280': Backobourkia_brouni
'281': Badumna_insignis
'282': Badumna_longinqua
'283': Bagheera_prosper
'284': Balaustium_leanderi
'285': Ballus_chalybeius
'286': Ballus_rufipes
'287': Barronopsis_texana
'288': Baryphas_ahenus
'289': Bassaniana_utahensis
'290': Bassaniana_versicolor
'291': Bassaniodes_bufo
'292': Bavia_sexpunctata
'293': Beata_wickhami
'294': Bidentolophus_bidens
'295': Bijoaraneus_mitificus
'296': Bijoaraneus_praesignis
'297': Bothriocyrtum_californicum
'298': Bothriurus_asper
'299': Bothriurus_bonariensis
'300': Brachypelma_albiceps
'301': Brachypelma_emilia
'302': Brachypelma_klaasi
'303': Brettus_cingulatus
'304': Brigittea_civica
'305': Brigittea_latens
'306': Bryantella_smaragda
'307': Burmattus_pococki
'308': Buthus_elongatus
'309': Buthus_ibericus
'310': Buthus_occitanus
'311': Buthus_pyrenaeus
'312': Caddo_agilis
'313': Caerostris_sexcuspidata
'314': Calisoga_longitarsis
'315': Callilepis_nocturna
'316': Callobius_bennetti
'317': Callobius_pictus
'318': Callobius_severus
'319': Camaricus_formosus
'320': Camaricus_maugei
'321': Cambridgea_foliata
'322': Carrhotus_sannio
'323': Carrhotus_viduus
'324': Carrhotus_xanthogramma
'325': Castianeira_amoena
'326': Castianeira_cingulata
'327': Castianeira_descripta
'328': Castianeira_longipalpa
'329': Castianeira_thalia
'330': Catalinia_andreas
'331': Catalinia_thompsoni
'332': Cecidophyes_nudus
'333': Cecidophyes_rouhollahi
'334': Celaenia_calotoides
'335': Celaenia_excavata
'336': Centroctenus_brevipes
'337': Centruroides_bicolor
'338': Centruroides_edwardsii
'339': Centruroides_elegans
'340': Centruroides_exilicauda
'341': Centruroides_fulvipes
'342': Centruroides_gracilis
'343': Centruroides_hentzi
'344': Centruroides_limbatus
'345': Centruroides_limpidus
'346': Centruroides_ochraceus
'347': Centruroides_ornatus
'348': Centruroides_sculpturatus
'349': Centruroides_suffusus
'350': Centruroides_vittatus
'351': Cercidia_prominens
'352': Cercophonius_squama
'353': Cesonia_bilineata
'354': Cetratus_rubropunctatus
'355': Chaetopelma_olivaceum
'356': Chalcoscirtus_diminutus
'357': Cheiracanthium_erraticum
'358': Cheiracanthium_gracile
'359': Cheiracanthium_inclusum
'360': Cheiracanthium_mildei
'361': Cheiracanthium_punctorium
'362': Chelifer_cancroides
'363': Chihuahuanus_coahuilae
'364': Chikunia_nigra
'365': Chira_gounellei
'366': Chira_lucina
'367': Chira_simoni
'368': Chira_spinosa
'369': Chrysilla_acerosa
'370': Chrysilla_lauta
'371': Chrysilla_volupe
'372': Cicurina_cicur
'373': Cithaeron_praedonius
'374': Clynotis_severus
'375': Colaranea_verutum
'376': Colaranea_viriditas
'377': Colomerus_vitis
'378': Colonus_hesperus
'379': Colonus_puerperus
'380': Colonus_sylvanus
'381': Coriarachne_depressa
'382': Corinnomma_severum
'383': Coryphasia_nigriventris
'384': Corythalia_argentinensis
'385': Corythalia_conferta
'386': Corythalia_opima
'387': Cosmobunus_granarius
'388': Cosmophasis_baehrae
'389': Cosmophasis_bitaeniata
'390': Cosmophasis_lami
'391': Cosmophasis_micarioides
'392': Cosmophasis_thalassina
'393': Cosmophasis_valerieae
'394': Cresmatoneta_mutinensis
'395': Crossopriza_lyoni
'396': Cryptachaea_blattea
'397': Cryptachaea_gigantipes
'398': Cryptachaea_veruculata
'399': Ctenus_amphora
'400': Ctenus_hibernalis
'401': Ctenus_medius
'402': Ctenus_ornatus
'403': Cupiennius_coccineus
'404': Cupiennius_getazi
'405': Cupiennius_salei
'406': Curicaberis_culiacan
'407': Cyclosa_bifida
'408': Cyclosa_bifurcata
'409': Cyclosa_caroli
'410': Cyclosa_conica
'411': Cyclosa_diversa
'412': Cyclosa_insulana
'413': Cyclosa_mulmeinensis
'414': Cyclosa_octotuberculata
'415': Cyclosa_oculata
'416': Cyclosa_trilobata
'417': Cyclosa_turbinata
'418': Cyclosa_walckenaeri
'419': Cymbacha_ocellata
'420': Cyrba_algerina
'421': Cyriocosmus_leetzi
'422': Cyrtarachne_inaequalis
'423': Cyrtarachne_ixoides
'424': Cyrtopholis_portoricae
'425': Cyrtophora_cicatrosa
'426': Cyrtophora_citricola
'427': Cyrtophora_exanthematica
'428': Cyrtophora_moluccensis
'429': Cyrtophora_unicolor
'430': Cytaea_alburna
'431': Cytaea_aspera
'432': Cytaea_dispalans
'433': Cytaea_maoming
'434': Dalquestia_formosa
'435': Damon_annulatipes
'436': Damon_variegatus
'437': Dasylobus_graniferus
'438': Davus_ruficeps
'439': Deinopis_longipes
'440': Deinopis_spinosa
'441': Delena_cancerides
'442': Deliochus_zelivira
'443': Dendrolycosa_icadia
'444': Dendryphantes_mordax
'445': Dendryphantes_rudis
'446': Dendryphantes_zygoballoides
'447': Dermacentor_andersoni
'448': Dermacentor_marginatus
'449': Dermacentor_occidentalis
'450': Dermacentor_reticulatus
'451': Dermacentor_variabilis
'452': Desis_marina
'453': Diaea_ambara
'454': Diaea_dorsata
'455': Diaea_livens
'456': Diapontia_uruguayensis
'457': Dicranopalpus_larvatus
'458': Dicranopalpus_ramosus
'459': Dictis_striatipes
'460': Dictyna_calcarata
'461': Diguetinus_raptator
'462': Dinothrombium_gigas
'463': Diplocentrus_lindo
'464': Dipoena_melanogaster
'465': Dolichothele_exilis
'466': Dolomedes_albineus
'467': Dolomedes_dondalei
'468': Dolomedes_facetus
'469': Dolomedes_fimbriatus
'470': Dolomedes_minor
'471': Dolomedes_mizhoanus
'472': Dolomedes_raptor
'473': Dolomedes_scriptus
'474': Dolomedes_striatus
'475': Dolomedes_sulfureus
'476': Dolomedes_tenebrosus
'477': Dolomedes_triton
'478': Dolomedes_vittatus
'479': Drapetisca_alteranda
'480': Drapetisca_socialis
'481': Dugesiella_anitahoffmannae
'482': Dysdera_crocata
'483': Ebrechtella_tricuspidata
'484': Edricus_productus
'485': Egaenus_convexus
'486': Elaver_excepta
'487': Enoplognatha_mandibularis
'488': Enoplognatha_ovata
'489': Epeus_flavobilineatus
'490': Epeus_glorius
'491': Epicadus_heterogaster
'492': Epicadus_trituberculatus
'493': Episinus_angulatus
'494': Episinus_maculipes
'495': Episinus_truncatus
'496': Epocilla_blairei
'497': Epocilla_calcarata
'498': Eratigena_agrestis
'499': Eratigena_atrica
'500': Eratigena_duellica
'501': Eratigena_inermis
'502': Eresus_kollari
'503': Eresus_sandaliatus
'504': Erginulus_subserialis
'505': Eriophora_edax
'506': Eriophora_fuliginea
'507': Eriophora_nephiloides
'508': Eriophora_ravilla
'509': Eriophyes_aceris
'510': Eriophyes_adenostomae
'511': Eriophyes_cerasicrumena
'512': Eriophyes_emarginatae
'513': Eriophyes_exilis
'514': Eriophyes_hoheriae
'515': Eriophyes_inangulis
'516': Eriophyes_laevis
'517': Eriophyes_leiosoma
'518': Eriophyes_paraviburni
'519': Eriophyes_pyri
'520': Eriophyes_rhoinus
'521': Eriophyes_similis
'522': Eriophyes_sorbi
'523': Eriophyes_tiliae
'524': Eriovixia_excelsa
'525': Eriovixia_laglaizei
'526': Eris_flava
'527': Eris_floridana
'528': Eris_militaris
'529': Ero_aphana
'530': Ero_tuberculata
'531': Euagrus_chisoseus
'532': Eucteniza_relata
'533': Eumesosoma_roeweri
'534': Euophrys_frontalis
'535': Euophrys_herbigrada
'536': Euophrys_monadnock
'537': Euophrys_rufibarbis
'538': Eupalaestrus_weijenberghi
'539': Euprosthenopsis_pulchella
'540': Euryattus_bleekeri
'541': Euryopis_episinoides
'542': Euryopis_funebris
'543': Eurytetranychus_buxi
'544': Euscorpius_flavicaudis
'545': Euscorpius_italicus
'546': Eusparassus_dufouri
'547': Eusparassus_walckenaeri
'548': Eustala_anastera
'549': Evarcha_albaria
'550': Evarcha_arcuata
'551': Evarcha_bulbosa
'552': Evarcha_falcata
'553': Evarcha_hoyi
'554': Evarcha_jucunda
'555': Evarcha_michailovi
'556': Evarcha_proszynskii
'557': Falconina_gracilis
'558': Filistata_insidiatrix
'559': Florinda_coccinea
'560': Forsteropsalis_inconstans
'561': Forsteropsalis_pureora
'562': Freya_nigrotaeniata
'563': Frigga_crocuta
'564': Frigga_pratensis
'565': Frigga_quintensis
'566': Frontinella_pyramitela
'567': Frontinellina_frutetorum
'568': Garypus_californicus
'569': Gasteracantha_cancriformis
'570': Gasteracantha_curvispina
'571': Gasteracantha_diadesmia
'572': Gasteracantha_diardi
'573': Gasteracantha_doriae
'574': Gasteracantha_falcicornis
'575': Gasteracantha_fornicata
'576': Gasteracantha_geminata
'577': Gasteracantha_kuhli
'578': Gasteracantha_milvoides
'579': Gasteracantha_quadrispinosa
'580': Gasteracantha_sacerdotalis
'581': Gasteracantha_sanguinolenta
'582': Gasteracantha_sauteri
'583': Gasteracantha_versicolor
'584': Gea_heptagon
'585': Gea_spinipes
'586': Gea_theridioides
'587': Geolycosa_vultuosa
'588': Gibbaranea_bituberculata
'589': Gibbaranea_gibbosa
'590': Gigantometrus_swammerdami
'591': Gladicosa_gulosa
'592': Gladicosa_pulchra
'593': Gluvia_dorsalis
'594': Graemeloweus_iviei
'595': Grammostola_rosea
'596': Gyas_annulatus
'597': Gyas_titanus
'598': Habrocestum_hongkongiense
'599': Habronattus_altanus
'600': Habronattus_americanus
'601': Habronattus_amicus
'602': Habronattus_borealis
'603': Habronattus_brunneus
'604': Habronattus_californicus
'605': Habronattus_captiosus
'606': Habronattus_clypeatus
'607': Habronattus_coecatus
'608': Habronattus_cognatus
'609': Habronattus_conjunctus
'610': Habronattus_cuspidatus
'611': Habronattus_decorus
'612': Habronattus_elegans
'613': Habronattus_fallax
'614': Habronattus_festus
'615': Habronattus_formosus
'616': Habronattus_forticulus
'617': Habronattus_hallani
'618': Habronattus_hirsutus
'619': Habronattus_jucundus
'620': Habronattus_klauseri
'621': Habronattus_mexicanus
'622': Habronattus_mustaciatus
'623': Habronattus_ophrys
'624': Habronattus_orbus
'625': Habronattus_oregonensis
'626': Habronattus_paratus
'627': Habronattus_peckhami
'628': Habronattus_pyrrithrix
'629': Habronattus_sansoni
'630': Habronattus_tarsalis
'631': Habronattus_texanus
'632': Habronattus_ustulatus
'633': Habronattus_viridipes
'634': Hadrobunus_maculosus
'635': Hadrurus_anzaborrego
'636': Hadrurus_arizonensis
'637': Hadrurus_spadix
'638': Hakka_himeshimensis
'639': Hamadruas_hieroglyphica
'640': Hamataliwa_grisea
'641': Harmochirus_brachiatus
'642': Harpactea_hombergi
'643': Harpactea_rubicunda
'644': Harpactira_atra
'645': Hasarius_adansoni
'646': Hebestatis_theveneti
'647': Heliophanus_apiatus
'648': Heliophanus_auratus
'649': Heliophanus_cupreus
'650': Heliophanus_hamifer
'651': Heliophanus_kochii
'652': Heliophanus_melinus
'653': Heliophanus_tribulosus
'654': Helpis_minitabunda
'655': Hemicloea_rogenhoferi
'656': Hentzia_grenada
'657': Hentzia_mitrata
'658': Hentzia_palmarum
'659': Herennia_multipuncta
'660': Heriaeus_hirtus
'661': Heriaeus_oblongus
'662': Herpyllus_ecclesiasticus
'663': Herpyllus_propinquus
'664': Hesperonemastoma_modestum
'665': Heterometrus_laoticus
'666': Heterometrus_longimanus
'667': Heterometrus_silenus
'668': Heterophrynus_batesii
'669': Heterophrynus_longicornis
'670': Heteropoda_amphora
'671': Heteropoda_boiei
'672': Heteropoda_davidbowie
'673': Heteropoda_jugulans
'674': Heteropoda_longipes
'675': Heteropoda_pingtungensis
'676': Heteropoda_procera
'677': Heteropoda_tetrica
'678': Heteropoda_venatoria
'679': Heterotheridion_nigrovariegatum
'680': Hibana_gracilis
'681': Hibana_incursa
'682': Hinewaia_embolica
'683': Hippasa_holmerae
'684': Hogna_antelucana
'685': Hogna_baltimoriana
'686': Hogna_bivittata
'687': Hogna_carolinensis
'688': Hogna_crispipes
'689': Hogna_frondicola
'690': Hogna_gumia
'691': Hogna_radiata
'692': Holcolaetis_zuluensis
'693': Holconia_immanis
'694': Holconia_insignis
'695': Holocnemus_pluchei
'696': Holoplatys_apressus
'697': Holoplatys_invenusta
'698': Holothele_longipes
'699': Homalenotus_quadridentatus
'700': Homalonychus_theologus
'701': Hortophora_biapicata
'702': Hortophora_tatianeae
'703': Hortophora_transmarina
'704': Hottentotta_judaicus
'705': Hottentotta_tamulus
'706': Hygropoda_lineata
'707': Hyllus_argyrotoxus
'708': Hyllus_brevitarsis
'709': Hyllus_diardi
'710': Hyllus_keratodes
'711': Hyllus_semicupreus
'712': Hyllus_treleaveni
'713': Hypochilus_pococki
'714': Hypodrassodes_maoricus
'715': Hypselistes_florens
'716': Hypsosinga_albovittata
'717': Hypsosinga_heri
'718': Hyptiotes_cavatus
'719': Hyptiotes_gertschi
'720': Hyptiotes_paradoxus
'721': Icius_hamatus
'722': Icius_subinermis
'723': Intruda_signata
'724': Iridopelma_hirsutum
'725': Irura_bidenticulata
'726': Isala_cambridgei
'727': Ischnothele_annulata
'728': Ischnothele_caudata
'729': Isometrus_maculatus
'730': Isopeda_leishmanni
'731': Isopeda_montana
'732': Isopeda_queenslandensis
'733': Isopeda_vasta
'734': Isopeda_villosa
'735': Isopedella_cerussata
'736': Isopedella_flavida
'737': Isopedella_leai
'738': Isopedella_pessleri
'739': Isopedella_victorialis
'740': Isoxya_cicatricosa
'741': Isoxya_tabulata
'742': Ixodes_holocyclus
'743': Ixodes_pacificus
'744': Ixodes_persulcatus
'745': Ixodes_ricinus
'746': Ixodes_scapularis
'747': Jaguajir_rochae
'748': Janula_bicornis
'749': Javanimetrus_cyaneus
'750': Jotus_auripes
'751': Jotus_frosti
'752': Judalana_lutea
'753': Kankuamo_marquezi
'754': Kelawakaju_frenata
'755': Kochiura_aulica
'756': Kovarikia_angelena
'757': Kovarikia_oxy
'758': Kovarikia_williamsi
'759': Krateromaspis_dilatata
'760': Kukulcania_arizonica
'761': Kukulcania_hibernalis
'762': Labulla_thoracica
'763': Lacinius_dentiger
'764': Lacinius_ephippiatus
'765': Lacinius_horridus
'766': Lampona_cylindrata
'767': Lampona_murina
'768': Larinia_borealis
'769': Larinia_directa
'770': Larinia_lineata
'771': Larinioides_cornutus
'772': Larinioides_ixobolus
'773': Larinioides_patagiatus
'774': Larinioides_sclopetarius
'775': Lathys_humilis
'776': Latonigena_auricomis
'777': Latrodectus_bishopi
'778': Latrodectus_curacaviensis
'779': Latrodectus_geometricus
'780': Latrodectus_hasselti
'781': Latrodectus_hesperus
'782': Latrodectus_katipo
'783': Latrodectus_mactans
'784': Latrodectus_mirabilis
'785': Latrodectus_renivulvatus
'786': Latrodectus_tredecimguttatus
'787': Latrodectus_variolus
'788': Leiobunum_aldrichi
'789': Leiobunum_bimaculatum
'790': Leiobunum_blackwalli
'791': Leiobunum_calcar
'792': Leiobunum_exilipes
'793': Leiobunum_flavum
'794': Leiobunum_gracile
'795': Leiobunum_limbatum
'796': Leiobunum_nigropalpi
'797': Leiobunum_rotundum
'798': Leiobunum_townsendi
'799': Leiobunum_uxorium
'800': Leiobunum_ventricosum
'801': Leiobunum_verrucosum
'802': Leiobunum_vittatum
'803': Leiurus_hebraeus
'804': Leptobunus_parvulus
'805': Leptofreya_ambigua
'806': Leptorchestes_berolinensis
'807': Leucauge_argentina
'808': Leucauge_argyra
'809': Leucauge_argyrobapta
'810': Leucauge_blanda
'811': Leucauge_celebesiana
'812': Leucauge_decorata
'813': Leucauge_dromedaria
'814': Leucauge_fastigata
'815': Leucauge_festiva
'816': Leucauge_granulata
'817': Leucauge_licina
'818': Leucauge_mariana
'819': Leucauge_regnyi
'820': Leucauge_tessellata
'821': Leucauge_venusta
'822': Leucauge_volupis
'823': Leuronychus_pacificus
'824': Leviana_dimidiata
'825': Leviellus_stroemi
'826': Ligurra_latidens
'827': Linyphia_hortensis
'828': Linyphia_triangularis
'829': Liocheles_australasiae
'830': Liocranum_rupicola
'831': Liophrurillus_flavitarsis
'832': Lophopilio_palpinalis
'833': Loxosceles_amazonica
'834': Loxosceles_deserta
'835': Loxosceles_laeta
'836': Loxosceles_reclusa
'837': Loxosceles_rufescens
'838': Loxosceles_tenochtitlan
'839': Loxosceles_yucatana
'840': Lupettiana_mordax
'841': Lurio_conspicuus
'842': Lychas_marmoreus
'843': Lychas_mucronatus
'844': Lychas_scutilus
'845': Lychas_variatus
'846': Lycosa_erythrognatha
'847': Lycosa_hispanica
'848': Lycosa_pampeana
'849': Lycosa_praegrandis
'850': Lycosa_singoriensis
'851': Lycosa_tarantula
'852': Lycosoides_coarctata
'853': Lyssomanes_pauper
'854': Lyssomanes_viridis
'855': Macaroeris_nidicolens
'856': Macracantha_arcuata
'857': Macracantha_hasselti
'858': Macrothele_calpeiana
'859': Maeota_dichrura
'860': Maevia_inclemens
'861': Maimuna_vestita
'862': Mangora_acalypha
'863': Mangora_gibberosa
'864': Mangora_maculata
'865': Mangora_placida
'866': Mangora_spiculata
'867': Manogea_porracea
'868': Maratus_anomalus
'869': Maratus_chrysomelas
'870': Maratus_expolitus
'871': Maratus_griseus
'872': Maratus_harrisi
'873': Maratus_karrie
'874': Maratus_leo
'875': Maratus_literatus
'876': Maratus_pavonis
'877': Maratus_plumosus
'878': Maratus_scutulatus
'879': Maratus_tasmanicus
'880': Maratus_vespertilio
'881': Maratus_volans
'882': Marchena_minuta
'883': Marma_nigritarsis
'884': Marpissa_formosa
'885': Marpissa_lineata
'886': Marpissa_muscosa
'887': Marpissa_nivoyi
'888': Marpissa_obtusa
'889': Marpissa_pikei
'890': Marpissa_radiata
'891': Massuria_simplex
'892': Mastigoproctus_giganteus
'893': Mastigoproctus_tohono
'894': Mastophora_cornigera
'895': Mastophora_phrynosoma
'896': Mecaphesa_asperata
'897': Mecaphesa_celer
'898': Mecynogea_lemniscata
'899': Megabunus_diadema
'900': Megadolomedes_trux
'901': Megafreya_sutrix
'902': Megahexura_fulva
'903': Megaphobema_mesomelas
'904': Megaphobema_velvetosoma
'905': Mendoza_canestrinii
'906': Menemerus_bivittatus
'907': Menemerus_nigli
'908': Menemerus_semilimbatus
'909': Menemerus_taeniatus
'910': Meriola_decepta
'911': Messua_limbata
'912': Meta_bourneti
'913': Meta_menardi
'914': Meta_ovalis
'915': Metacyrba_floridana
'916': Metacyrba_punctata
'917': Metacyrba_taeniola
'918': Metaltella_simoni
'919': Metaphalangium_cirtanum
'920': Metaphidippus_chera
'921': Metaphidippus_manni
'922': Metaphidippus_siticulosus
'923': Metaplatybunus_grandissimus
'924': Metazygia_wittfeldae
'925': Metazygia_zilloides
'926': Metellina_curtisi
'927': Metellina_mengei
'928': Metellina_merianae
'929': Metellina_segmentata
'930': Metepeira_labyrinthea
'931': Mexcala_elegans
'932': Mexigonus_minutus
'933': Micrathena_acuta
'934': Micrathena_brevipes
'935': Micrathena_clypeata
'936': Micrathena_crassa
'937': Micrathena_duodecimspinosa
'938': Micrathena_fissispina
'939': Micrathena_funebris
'940': Micrathena_furcata
'941': Micrathena_gracilis
'942': Micrathena_horrida
'943': Micrathena_kirbyi
'944': Micrathena_lucasi
'945': Micrathena_mitrata
'946': Micrathena_nigrichelis
'947': Micrathena_patruelis
'948': Micrathena_pichincha
'949': Micrathena_picta
'950': Micrathena_plana
'951': Micrathena_pungens
'952': Micrathena_raimondi
'953': Micrathena_saccata
'954': Micrathena_sagittata
'955': Micrathena_sanctispiritus
'956': Micrathena_schreibersi
'957': Micrathena_sexspinosa
'958': Micrathena_swainsoni
'959': Micrathena_triangularis
'960': Micrathena_vigorsi
'961': Microlinyphia_dana
'962': Microlinyphia_pusilla
'963': Micrommata_ligurina
'964': Micrommata_virescens
'965': Micropholcus_fauroti
'966': Mimetus_laevigatus
'967': Mimetus_puritanus
'968': Mischonyx_squalidus
'969': Missulena_bradleyi
'970': Missulena_occatoria
'971': Misumena_spinifera
'972': Misumena_vatia
'973': Misumenoides_formosipes
'974': Misumenops_callinurus
'975': Misumenops_maculissparsus
'976': Misumenops_rubrodecoratus
'977': Misumessus_oblongus
'978': Mitopus_glacialis
'979': Mitopus_morio
'980': Mitostoma_chrysomelas
'981': Mituliodon_tarantulinus
'982': Molinaranea_clymene
'983': Monaeses_paradoxus
'984': Mopsus_mormon
'985': Myrmaplata_plataleoides
'986': Myrmarachne_formicaria
'987': Myrmarachne_formosana
'988': Myrmarachne_ichneumon
'989': Myrmarachne_japonica
'990': Myrmarachne_melanocephala
'991': Myrmekiaphila_comstocki
'992': Nanometa_lagenifera
'993': Naphrys_acerba
'994': Naphrys_pulex
'995': Natta_horizontalis
'996': Nelima_doriae
'997': Nelima_paessleri
'998': Nemastoma_bimaculatum
'999': Nemastoma_lugubre
'1000': Neomolgus_littoralis
'1001': Neopantopsalis_pentheter
'1002': Neoscona_adianta
'1003': Neoscona_arabesca
'1004': Neoscona_byzanthina
'1005': Neoscona_crucifera
'1006': Neoscona_domiciliorum
'1007': Neoscona_mellotteei
'1008': Neoscona_moreli
'1009': Neoscona_nautica
'1010': Neoscona_oaxacensis
'1011': Neoscona_orizabensis
'1012': Neoscona_punctigera
'1013': Neoscona_scylla
'1014': Neoscona_scylloides
'1015': Neoscona_subfusca
'1016': Neoscona_theisi
'1017': Neoscona_triangula
'1018': Neoscona_vigilans
'1019': Neosparassus_calligaster
'1020': Neosparassus_diana
'1021': Neosparassus_magareyi
'1022': Neosparassus_patellatus
'1023': Neosparassus_salacius
'1024': Neospintharus_trigonum
'1025': Neotama_mexicana
'1026': Neottiura_bimaculata
'1027': Nephila_kuhli
'1028': Nephila_pilipes
'1029': Nephila_vitiana
'1030': Nephilengys_malabarensis
'1031': Nephilengys_papuana
'1032': Nephilingis_cruentata
'1033': Nephilingis_livida
'1034': Neriene_clathrata
'1035': Neriene_digna
'1036': Neriene_emphana
'1037': Neriene_litigiosa
'1038': Neriene_montana
'1039': Neriene_peltata
'1040': Neriene_radiata
'1041': Nesticodes_rufipes
'1042': Nesticus_cellulanus
'1043': Nicodamus_peregrinus
'1044': Nigma_flavescens
'1045': Nigma_linsdalei
'1046': Nigma_puella
'1047': Nigma_walckenaeri
'1048': Nihonhimea_mundula
'1049': Nihonhimea_tesselata
'1050': Nilus_albocinctus
'1051': Novakiella_trituberculosa
'1052': Novaranea_queribunda
'1053': Nuctenea_umbratica
'1054': Nungia_epigynalis
'1055': Nurscia_albomaculata
'1056': Nycerella_delecta
'1057': Nyssus_albopunctatus
'1058': Nyssus_coloripes
'1059': Ocrisiona_leucocomis
'1060': Odiellus_lendlii
'1061': Odiellus_pictus
'1062': Odiellus_spinosus
'1063': Oecobius_maculatus
'1064': Oecobius_navus
'1065': Oligolophus_hansenii
'1066': Oligolophus_tridens
'1067': Olios_argelasius
'1068': Olios_giganteus
'1069': Olios_lamarcki
'1070': Opilio_canestrinii
'1071': Opilio_parietinus
'1072': Opilio_saxatilis
'1073': Opisthacanthus_asper
'1074': Opisthacanthus_capensis
'1075': Opisthacanthus_validus
'1076': Opisthoncus_abnormis
'1077': Opisthoncus_nigrofemoratus
'1078': Opisthoncus_polyphemus
'1079': Opisthoncus_quadratarius
'1080': Opisthoncus_serratofasciatus
'1081': Opisthoncus_sexmaculatus
'1082': Opistophthalmus_capensis
'1083': Opistophthalmus_carinatus
'1084': Opistophthalmus_glabrifrons
'1085': Opistophthalmus_karrooensis
'1086': Opistophthalmus_macer
'1087': Opistophthalmus_pallipes
'1088': Opistophthalmus_pugnax
'1089': Opistophthalmus_wahlbergii
'1090': Ordgarius_magnificus
'1091': Ortholasma_rugosum
'1092': Ostearius_melanopygius
'1093': Oxyopes_amoenus
'1094': Oxyopes_flavipalpis
'1095': Oxyopes_gracilipes
'1096': Oxyopes_heterophthalmus
'1097': Oxyopes_lineatus
'1098': Oxyopes_macilentus
'1099': Oxyopes_ramosus
'1100': Oxyopes_salticus
'1101': Oxyopes_scalaris
'1102': Oxyopes_sertatus
'1103': Oxyopes_shweta
'1104': Oxyopes_tridens
'1105': Oxyopes_variabilis
'1106': Oxytate_striatipes
'1107': Oxytate_virens
'1108': Ozyptila_pacifica
'1109': Ozyptila_praticola
'1110': Pachygnatha_autumnalis
'1111': Pachygnatha_clercki
'1112': Pachygnatha_degeeri
'1113': Pachygnatha_listeri
'1114': Pachyloides_thorellii
'1115': Paidiscura_pallens
'1116': Palpimanus_gibbulus
'1117': Palystes_castaneus
'1118': Palystes_superciliosus
'1119': Pancorius_crassipes
'1120': Pandercetes_gracilis
'1121': Parabuthus_capensis
'1122': Parabuthus_granulatus
'1123': Parabuthus_planicauda
'1124': Parabuthus_raudus
'1125': Parabuthus_transvaalicus
'1126': Parabuthus_villosus
'1127': Paramaevia_poultoni
'1128': Paranemastoma_quadripunctatum
'1129': Paraphidippus_aurantius
'1130': Paraphidippus_fartilis
'1131': Paraphilaeus_daemeli
'1132': Paraphrynus_carolynae
'1133': Paraphrynus_laevifrons
'1134': Parasteatoda_lunata
'1135': Parasteatoda_tepidariorum
'1136': Parasynema_cirripes
'1137': Paratrochosina_amica
'1138': Paravaejovis_confusus
'1139': Paravaejovis_puritanus
'1140': Paravaejovis_spinigerus
'1141': Paravaejovis_waeringi
'1142': Parawixia_audax
'1143': Parawixia_bistriata
'1144': Parawixia_dehaani
'1145': Pardosa_amentata
'1146': Pardosa_lapidicina
'1147': Pardosa_mercurialis
'1148': Pardosa_moesta
'1149': Pardosa_wagleri
'1150': Parnaenus_cyanidens
'1151': Paroligolophus_agrestis
'1152': Paruroctonus_becki
'1153': Paruroctonus_boreus
'1154': Paruroctonus_silvestrii
'1155': Pediana_regina
'1156': Pelegrina_aeneola
'1157': Pelegrina_balia
'1158': Pelegrina_exigua
'1159': Pelegrina_flavipes
'1160': Pelegrina_galathea
'1161': Pelegrina_pervaga
'1162': Pelegrina_proterva
'1163': Pellenes_allegrii
'1164': Pellenes_geniculatus
'1165': Pellenes_nigrociliatus
'1166': Pellenes_seriatus
'1167': Pellenes_tripunctatus
'1168': Penthaleus_major
'1169': Peucetia_flava
'1170': Peucetia_longipalpis
'1171': Peucetia_rubrolineata
'1172': Peucetia_viridana
'1173': Peucetia_viridans
'1174': Peucetia_viridis
'1175': Phaeacius_malayensis
'1176': Phalangium_opilio
'1177': Phanias_albeolus
'1178': Phiale_formosa
'1179': Phiale_gratiosa
'1180': Phiale_guttata
'1181': Phiale_mimica
'1182': Phiale_roburifoliata
'1183': Phiale_tristis
'1184': Phidippus_adumbratus
'1185': Phidippus_apacheanus
'1186': Phidippus_ardens
'1187': Phidippus_arizonensis
'1188': Phidippus_asotus
'1189': Phidippus_audax
'1190': Phidippus_borealis
'1191': Phidippus_californicus
'1192': Phidippus_cardinalis
'1193': Phidippus_carneus
'1194': Phidippus_carolinensis
'1195': Phidippus_clarus
'1196': Phidippus_comatus
'1197': Phidippus_cruentus
'1198': Phidippus_cryptus
'1199': Phidippus_insignarius
'1200': Phidippus_johnsoni
'1201': Phidippus_mystaceus
'1202': Phidippus_nikites
'1203': Phidippus_octopunctatus
'1204': Phidippus_olympus
'1205': Phidippus_otiosus
'1206': Phidippus_pacosauritus
'1207': Phidippus_phoenix
'1208': Phidippus_pius
'1209': Phidippus_princeps
'1210': Phidippus_purpuratus
'1211': Phidippus_putnami
'1212': Phidippus_regius
'1213': Phidippus_texanus
'1214': Phidippus_tyrrelli
'1215': Phidippus_whitmani
'1216': Phidippus_workmani
'1217': Philaeus_chrysops
'1218': Philira_micans
'1219': Philodromus_dispar
'1220': Philodromus_fuscomarginatus
'1221': Philodromus_margaritatus
'1222': Philodromus_marxi
'1223': Philodromus_poecilus
'1224': Philodromus_rufus
'1225': Philoponella_congregabilis
'1226': Phintella_accentifera
'1227': Phintella_aequipes
'1228': Phintella_bifurcilinea
'1229': Phintella_castriesiana
'1230': Phintella_piatensis
'1231': Phintella_vittata
'1232': Phintelloides_versicolor
'1233': Phlegra_bresnieri
'1234': Phlegra_fasciata
'1235': Phlegra_hentzi
'1236': Pholcus_manueli
'1237': Pholcus_opilionoides
'1238': Pholcus_phalangioides
'1239': Phoneutria_boliviensis
'1240': Phoneutria_depilata
'1241': Phoneutria_fera
'1242': Phoneutria_nigriventer
'1243': Phoneutria_pertyi
'1244': Phoneutria_reidyi
'1245': Phonognatha_graeffei
'1246': Phoroncidia_sextuberculata
'1247': Phrixotrichus_vulpinus
'1248': Phrurolithus_festivus
'1249': Phrynarachne_ceylonica
'1250': Phrynarachne_katoi
'1251': Phrynarachne_rugosa
'1252': Phrynus_operculatus
'1253': Phycosoma_digitula
'1254': Phyllocoptes_didelphis
'1255': Phyllocoptes_eupadi
'1256': Phyllocoptes_goniothorax
'1257': Phyllocoptes_populi
'1258': Phylloneta_impressa
'1259': Phylloneta_pictipes
'1260': Physocyclus_globosus
'1261': Phytoptus_avellanae
'1262': Pimoa_altioculata
'1263': Pirata_piraticus
'1264': Pisaura_mirabilis
'1265': Pisaurina_dubia
'1266': Pisaurina_mira
'1267': Pisaurina_undulata
'1268': Pistius_truncatus
'1269': Pityohyphantes_phrygianus
'1270': Platnickina_mneon
'1271': Platnickina_tincta
'1272': Platybunus_pinetorum
'1273': Platycryptus_californicus
'1274': Platycryptus_undatus
'1275': Platyoides_walteri
'1276': Plebs_bradleyi
'1277': Plebs_eburnus
'1278': Plexippus_paykulli
'1279': Plexippus_petersi
'1280': Plexippus_setipes
'1281': Poecilopachys_australasia
'1282': Polybetes_pythagoricus
'1283': Polybetes_rapidus
'1284': Porrhothele_antipodiana
'1285': Portacosa_cinerea
'1286': Portia_schultzi
'1287': Poultonella_alboimmaculata
'1288': Prosoponoides_sinense
'1289': Prostheclina_amplior
'1290': Prostheclina_pallida
'1291': Protolophus_singularis
'1292': Psalmopoeus_cambridgei
'1293': Psalmopoeus_reduncus
'1294': Pseudeuophrys_erratica
'1295': Pseudeuophrys_lanigera
'1296': Pseudeuophrys_vafra
'1297': Pseudicius_encarpatus
'1298': Pseudogagrella_splendens
'1299': Pseudolychas_ochraceus
'1300': Pseudouroctonus_reddelli
'1301': Psilochorus_simoni
'1302': Pterinopelma_longisternale
'1303': Pterinopelma_roseum
'1304': Ptocasius_strupifer
'1305': Ptocasius_weyersi
'1306': Pulchellodromus_bistigma
'1307': Pulchellodromus_pulchellus
'1308': Pungalina_plurilineata
'1309': Pystira_ephippigera
'1310': Rabidosa_hentzi
'1311': Rabidosa_punctulata
'1312': Rabidosa_rabida
'1313': Rhene_flavicomans
'1314': Rhene_flavigera
'1315': Rhene_rubrigera
'1316': Rhipicephalus_sanguineus
'1317': Rhomphaea_fictilium
'1318': Rhomphaea_projiciens
'1319': Rhomphaea_urquharti
'1320': Rhysodromus_histrio
'1321': Rilaena_triangularis
'1322': Ruborridion_musivum
'1323': Runcinia_acuminata
'1324': Runcinia_grammica
'1325': Runcinioides_litteratus
'1326': Sadocus_asperatus
'1327': Sadocus_polyacanthus
'1328': Saitis_barbipes
'1329': Saitis_tauricus
'1330': Saitis_variegatus
'1331': Saitis_virgatus
'1332': Salpesia_squalida
'1333': Salsa_brisbanae
'1334': Salsa_fuliginata
'1335': Salticus_austinensis
'1336': Salticus_cingulatus
'1337': Salticus_mutabilis
'1338': Salticus_palpalis
'1339': Salticus_peckhamae
'1340': Salticus_propinquus
'1341': Salticus_scenicus
'1342': Salticus_zebraneus
'1343': Sandalodes_bipenicillatus
'1344': Sandalodes_scopifer
'1345': Sandalodes_superbus
'1346': Saphrys_rusticana
'1347': Sardinidion_blackwalli
'1348': Sarinda_hentzi
'1349': Sassacus_cyaneus
'1350': Sassacus_papenhoei
'1351': Sassacus_vitis
'1352': Schizocosa_avida
'1353': Schizocosa_malitiosa
'1354': Schizocosa_mccooki
'1355': Sclerobunus_nondimorphicus
'1356': Scotina_celans
'1357': Scotophaeus_blackwalli
'1358': Scytodes_atlacoya
'1359': Scytodes_fusca
'1360': Scytodes_globula
'1361': Scytodes_pallida
'1362': Scytodes_thoracica
'1363': Scytodes_univittata
'1364': Segestria_bavarica
'1365': Segestria_florentina
'1366': Segestria_pacifica
'1367': Segestria_senoculata
'1368': Selenops_mexicanus
'1369': Selenops_submaculosus
'1370': Sergiolus_capulatus
'1371': Sergiolus_montanus
'1372': Serradigitus_gertschi
'1373': Servaea_incana
'1374': Servaea_villosa
'1375': Sicarius_thomisoides
'1376': Sidymella_angularis
'1377': Sidymella_hirsuta
'1378': Sidymella_longipes
'1379': Sidymella_rubrosignata
'1380': Sidymella_trapezia
'1381': Siler_collingwoodi
'1382': Siler_cupreus
'1383': Siler_semiglaucus
'1384': Simaetha_tenuidens
'1385': Simitidion_simile
'1386': Singa_hamata
'1387': Singa_nitidula
'1388': Siro_rubens
'1389': Sittisax_ranieri
'1390': Smeringopus_pallidus
'1391': Smeringurus_mesaensis
'1392': Smeringurus_vachoni
'1393': Socca_pustulosa
'1394': Soerensenella_prehensor
'1395': Sosippus_californicus
'1396': Spartaeus_spinimanus
'1397': Spermophora_senoculata
'1398': Sphodros_niger
'1399': Sphodros_rufipes
'1400': Spintharus_flavidus
'1401': Spiracme_striatipes
'1402': Steatoda_albomaculata
'1403': Steatoda_ancorata
'1404': Steatoda_bipunctata
'1405': Steatoda_borealis
'1406': Steatoda_capensis
'1407': Steatoda_castanea
'1408': Steatoda_grossa
'1409': Steatoda_lepida
'1410': Steatoda_nobilis
'1411': Steatoda_paykulliana
'1412': Steatoda_triangulosa
'1413': Stegodyphus_dumicola
'1414': Stegodyphus_lineatus
'1415': Stegodyphus_sarasinorum
'1416': Stemonyphantes_lineatus
'1417': Stenacis_euonymi
'1418': Stenacis_triradiata
'1419': Stephanopis_altifrons
'1420': Stephanopis_barbipes
'1421': Stephanopis_carcinoides
'1422': Stiphidion_facetum
'1423': Strigoplus_guizhouensis
'1424': Sumampattus_quinqueradiatus
'1425': Superstitionia_donensis
'1426': Synageles_venator
'1427': Synema_globosum
'1428': Synema_imitatrix
'1429': Synema_parvulum
'1430': Synemosyna_formica
'1431': Talavera_minuta
'1432': Tamopsis_brisbanensis
'1433': Tamopsis_fickerti
'1434': Tamopsis_tweedensis
'1435': Tapinillus_longipes
'1436': Tarkas_maculatipes
'1437': Tegenaria_domestica
'1438': Tegenaria_ferruginea
'1439': Tegenaria_parietina
'1440': Telamonia_caprina
'1441': Telamonia_dimidiata
'1442': Telamonia_festiva
'1443': Telamonia_vlijmi
'1444': Telaprocera_maudae
'1445': Teminius_affinis
'1446': Teminius_insularis
'1447': Terralonus_californicus
'1448': Tetragnatha_extensa
'1449': Tetragnatha_hasselti
'1450': Tetragnatha_laboriosa
'1451': Tetragnatha_montana
'1452': Tetragnatha_obtusa
'1453': Tetragnatha_squamata
'1454': Tetragnatha_versicolor
'1455': Tetragnatha_viridis
'1456': Tetranychus_lintearius
'1457': Tetranychus_urticae
'1458': Teuthraustes_atramentarius
'1459': Textrix_denticulata
'1460': Thanatus_formicinus
'1461': Tharpyna_campestrata
'1462': Tharpyna_decorata
'1463': Thaumasia_velox
'1464': Thelacantha_brevispina
'1465': Thelcticopis_severa
'1466': Theraphosa_blondi
'1467': Theridion_pyramidale
'1468': Theridion_varians
'1469': Theridion_zonulatum
'1470': Theridiosoma_gemmosum
'1471': Theridula_emertoni
'1472': Theridula_gonygaster
'1473': Thiania_bhamoensis
'1474': Thiania_suboppressa
'1475': Thomisus_citrinellus
'1476': Thomisus_labefactus
'1477': Thomisus_onustus
'1478': Thomisus_scrupeus
'1479': Thomisus_spectabilis
'1480': Thorelliola_ensifera
'1481': Thwaitesia_margaritifera
'1482': Thwaitesia_nigronodosa
'1483': Thyene_coccineovittata
'1484': Thyene_imperialis
'1485': Thyene_inflata
'1486': Thyene_natalii
'1487': Thyene_ogdeni
'1488': Thyene_orientalis
'1489': Tibellus_oblongus
'1490': Tigrosa_annexa
'1491': Tigrosa_aspersa
'1492': Tigrosa_georgicola
'1493': Tigrosa_helluo
'1494': Tinus_peregrinus
'1495': Titanattus_andinus
'1496': Tityus_carrilloi
'1497': Tityus_columbianus
'1498': Tityus_metuendus
'1499': Tityus_obscurus
'1500': Tityus_serrulatus
'1501': Tityus_stigmurus
'1502': Tliltocatl_epicureanus
'1503': Tliltocatl_kahlenbergi
'1504': Tliltocatl_vagans
'1505': Tmarus_angulatus
'1506': Tmarus_piger
'1507': Togwoteeus_biceps
'1508': Tomopisthes_horrendus
'1509': Toxeus_magnus
'1510': Toxeus_maxillosus
'1511': Toxopsoides_huttoni
'1512': Trachelas_pacificus
'1513': Trachelas_tranquillus
'1514': Trachyzelotes_pedestris
'1515': Trichonephila_antipodiana
'1516': Trichonephila_clavata
'1517': Trichonephila_clavipes
'1518': Trichonephila_edulis
'1519': Trichonephila_fenestrata
'1520': Trichonephila_inaurata
'1521': Trichonephila_plumipes
'1522': Trichonephila_senegalensis
'1523': Trichonephila_sexpunctata
'1524': Trite_auricoma
'1525': Trite_mustilina
'1526': Trite_planiceps
'1527': Trochosa_ruricola
'1528': Trochosa_sepulchralis
'1529': Trochosa_terricola
'1530': Tropicosa_moesta
'1531': Tutelina_elegans
'1532': Tutelina_harti
'1533': Tutelina_similis
'1534': Tylorida_striata
'1535': Tylorida_ventralis
'1536': Typopeltis_crucifer
'1537': Typostola_barbata
'1538': Uliodon_albopunctatus
'1539': Uloborus_diversus
'1540': Uloborus_glomosus
'1541': Uloborus_plumipes
'1542': Uloborus_walckenaerius
'1543': Ummidia_audouini
'1544': Uroctea_durandi
'1545': Uroctonites_montereus
'1546': Uroctonus_mordax
'1547': Urodacus_manicatus
'1548': Urodacus_novaehollandiae
'1549': Uroplectes_carinatus
'1550': Uroplectes_flavoviridis
'1551': Uroplectes_formosus
'1552': Uroplectes_lineatus
'1553': Uroplectes_planimanus
'1554': Uroplectes_triangulifer
'1555': Uroplectes_vittatus
'1556': Vaejovis_carolinianus
'1557': Vaejovis_deboerae
'1558': Vaejovis_mexicanus
'1559': Varroa_destructor
'1560': Vasates_aceriscrumena
'1561': Vasates_quadripedes
'1562': Vectius_niger
'1563': Venator_immansuetus
'1564': Venator_spenceri
'1565': Venatrix_furcillata
'1566': Ventrivomer_ancyrophorus
'1567': Verrucosa_arenata
'1568': Verrucosa_meridionalis
'1569': Verrucosa_scapofracta
'1570': Verrucosa_undecimvariolata
'1571': Viciria_pavesii
'1572': Vicirionessa_mustela
'1573': Vonones_sayi
'1574': Wadicosa_fidelis
'1575': Wagneriana_spicata
'1576': Wagneriana_tauricornis
'1577': Witica_crassicauda
'1578': Wulfila_albens
'1579': Wulfila_saltabundus
'1580': Xerolycosa_miniata
'1581': Xerolycosa_nemoralis
'1582': Xysticus_cristatus
'1583': Xysticus_kochi
'1584': Xysticus_lanio
'1585': Xysticus_punctatus
'1586': Xysticus_texanus
'1587': Xysticus_ulmi
'1588': Yllenus_arenarius
'1589': Yllenus_uiguricus
'1590': Yllenus_zyuzini
'1591': Yunohamella_lyrica
'1592': Zachaeus_crista
'1593': Zealaranea_crassa
'1594': Zenodorus_orbiculatus
'1595': Zenodorus_swiftorum
'1596': Zilla_diodia
'1597': Zimiris_doriae
'1598': Zora_spinimana
'1599': Zoropsis_spinimana
'1600': Zosis_geniculata
'1601': Zygiella_atrica
'1602': Zygiella_x-notata
'1603': Zygoballus_nervosus
'1604': Zygoballus_rufipes
'1605': Zygoballus_sexpunctatus
'1606': Zygometis_xanthogaster
splits:
- name: train
num_bytes: 38785458087.024
num_examples: 2049928
download_size: 41403446692
dataset_size: 38785458087.024
---
# Dataset Card for "inatSpiders"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Coldog2333/super_dialseg | ---
license: apache-2.0
language:
- en
tags:
- dialogue segmentation
size_categories:
- 1K<n<10K
---
# Dataset Card for SuperDialseg
## 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:**
- **Repository:**
- **Paper:** SuperDialseg: A Large-scale Dataset for Supervised Dialogue Segmentation
- **Leaderboard:** [https://github.com/Coldog2333/SuperDialseg](https://github.com/Coldog2333/SuperDialseg)
- **Point of Contact:** jiangjf@is.s.u-tokyo.ac.jp
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages: English
## Dataset Structure
### Data Instances
```
{
"dial_data": {
"super_dialseg": [
{
"dial_id": "8df07b7a98990db27c395cb1f68a962e",
"turns": [
{
"da": "query_condition",
"role": "user",
"turn_id": 0,
"utterance": "Hello, I forgot o update my address, can you help me with that?",
"topic_id": 0,
"segmentation_label": 0
},
...
{
"da": "respond_solution",
"role": "agent",
"turn_id": 11,
"utterance": "DO NOT contact the New York State DMV to dispute whether you violated a toll regulation or failed to pay the toll , fees or other charges",
"topic_id": 4,
"segmentation_label": 0
}
],
...
}
]
}
```
### Data Fields
#### Dialogue-Level
+ `dial_id`: ID of a dialogue;
+ `turns`: All utterances of a dialogue.
#### Utterance-Level
+ `da`: Dialogue Act annotation derived from the original DGDS dataset;
+ `role`: Role annotation derived from the original DGDS dataset;
+ `turn_id`: ID of an utterance;
+ `utterance`: Text of the utterance;
+ `topic_id`: ID (order) of the current topic;
+ `segmentation_label`: 1: it is the end of a topic; 0: others.
### Data Splits
SuperDialseg follows the dataset splits of the original DGDS dataset.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
SuperDialseg was built on the top of doc2dial and MultiDoc2dial datasets.
Please refer to the original papers for more details.
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
The annotation of dialogue segmentation points is constructed by a set of well-designed strategy. Please refer to the paper for more details.
Other annotations like Dialogue Act and Role information are derived from doc2dial and MultiDoc2dial datasets.
### 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
Apache License Version 2.0, following the licenses of doc2dial and MultiDoc2dial.
### Citation Information
Coming soon
### Contributions
Thanks to [@Coldog2333](https://github.com/Coldog2333) for adding this dataset.
|
aditya6332/himym | ---
license: mit
---
|
vietgpt/truyencotich_vi | ---
dataset_info:
features:
- name: title
dtype: string
- name: content
dtype: string
- name: metadata
struct:
- name: type
dtype: string
- name: url
dtype: string
splits:
- name: train
num_bytes: 23491738
num_examples: 5444
download_size: 12096101
dataset_size: 23491738
---
# Dataset Card for "truyencotich_vi"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
bigscience-data/roots_indic-ml_wikiquote | ---
language: ml
license: cc-by-sa-3.0
extra_gated_prompt: 'By accessing this dataset, you agree to abide by the BigScience
Ethical Charter. The charter can be found at:
https://hf.co/spaces/bigscience/ethical-charter'
extra_gated_fields:
I have read and agree to abide by the BigScience Ethical Charter: checkbox
---
ROOTS Subset: roots_indic-ml_wikiquote
# wikiquote_filtered
- Dataset uid: `wikiquote_filtered`
### Description
### Homepage
### Licensing
### Speaker Locations
### Sizes
- 0.0462 % of total
- 0.1697 % of en
- 0.0326 % of fr
- 0.0216 % of ar
- 0.0066 % of zh
- 0.0833 % of pt
- 0.0357 % of es
- 0.0783 % of indic-ta
- 0.0361 % of indic-hi
- 0.0518 % of ca
- 0.0405 % of vi
- 0.0834 % of indic-ml
- 0.0542 % of indic-te
- 0.1172 % of indic-gu
- 0.0634 % of indic-kn
- 0.0539 % of id
- 0.0454 % of indic-ur
- 0.0337 % of indic-mr
- 0.0347 % of eu
### BigScience processing steps
#### Filters applied to: en
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_en
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_1024
#### Filters applied to: fr
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_fr
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_1024
#### Filters applied to: ar
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_ar
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_300
#### Filters applied to: zh
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_zhs
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_1024
#### Filters applied to: pt
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_pt
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_300
#### Filters applied to: es
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_es
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_1024
#### Filters applied to: indic-ta
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_indic-ta
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_300
#### Filters applied to: indic-hi
- dedup_document
- filter_remove_empty_docs
- split_sentences_indic-hi
- dedup_template_soft
- filter_small_docs_bytes_300
#### Filters applied to: ca
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_ca
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_1024
#### Filters applied to: vi
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_vi
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_300
#### Filters applied to: indic-ml
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_indic-ml
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_300
#### Filters applied to: indic-te
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_indic-te
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_300
#### Filters applied to: indic-gu
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_indic-gu
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_300
#### Filters applied to: indic-kn
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_indic-kn
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_300
#### Filters applied to: id
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_id
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_300
#### Filters applied to: indic-ur
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- dedup_template_soft
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-mr
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_indic-mr
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_300
#### Filters applied to: eu
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_eu
- dedup_template_soft
- replace_newline_with_space
|
Mitsuki-Sakamoto/alpaca_farm-deberta-re-pref-64-_fil_self_160m_bo16_2_mix_50_kl_0.1_prm_160m_thr_0.1_seed_1 | ---
dataset_info:
config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
- name: preference
dtype: int64
- name: output_1
dtype: string
- name: output_2
dtype: string
- name: reward_model_prompt_format
dtype: string
- name: gen_prompt_format
dtype: string
- name: gen_kwargs
struct:
- name: do_sample
dtype: bool
- name: max_new_tokens
dtype: int64
- name: pad_token_id
dtype: int64
- name: top_k
dtype: int64
- name: top_p
dtype: float64
- name: reward_1
dtype: float64
- name: reward_2
dtype: float64
- name: n_samples
dtype: int64
- name: reject_select
dtype: string
- name: index
dtype: int64
- name: prompt
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
- name: filtered_epoch
dtype: int64
- name: gen_reward
dtype: float64
- name: gen_response
dtype: string
splits:
- name: epoch_0
num_bytes: 43769030
num_examples: 18928
- name: epoch_1
num_bytes: 44331254
num_examples: 18928
- name: epoch_2
num_bytes: 44381950
num_examples: 18928
- name: epoch_3
num_bytes: 44407942
num_examples: 18928
- name: epoch_4
num_bytes: 44426328
num_examples: 18928
- name: epoch_5
num_bytes: 44426002
num_examples: 18928
- name: epoch_6
num_bytes: 44413607
num_examples: 18928
- name: epoch_7
num_bytes: 44399461
num_examples: 18928
- name: epoch_8
num_bytes: 44393274
num_examples: 18928
- name: epoch_9
num_bytes: 44390190
num_examples: 18928
- name: epoch_10
num_bytes: 44381678
num_examples: 18928
- name: epoch_11
num_bytes: 44382751
num_examples: 18928
- name: epoch_12
num_bytes: 44377189
num_examples: 18928
- name: epoch_13
num_bytes: 44377571
num_examples: 18928
- name: epoch_14
num_bytes: 44377633
num_examples: 18928
- name: epoch_15
num_bytes: 44377504
num_examples: 18928
- name: epoch_16
num_bytes: 44374952
num_examples: 18928
- name: epoch_17
num_bytes: 44378130
num_examples: 18928
- name: epoch_18
num_bytes: 44374143
num_examples: 18928
- name: epoch_19
num_bytes: 44375557
num_examples: 18928
- name: epoch_20
num_bytes: 44375702
num_examples: 18928
- name: epoch_21
num_bytes: 44375558
num_examples: 18928
- name: epoch_22
num_bytes: 44373811
num_examples: 18928
- name: epoch_23
num_bytes: 44375745
num_examples: 18928
- name: epoch_24
num_bytes: 44375296
num_examples: 18928
- name: epoch_25
num_bytes: 44375269
num_examples: 18928
- name: epoch_26
num_bytes: 44376057
num_examples: 18928
- name: epoch_27
num_bytes: 44376286
num_examples: 18928
- name: epoch_28
num_bytes: 44374887
num_examples: 18928
- name: epoch_29
num_bytes: 44373987
num_examples: 18928
download_size: 701250431
dataset_size: 1330868744
configs:
- config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1
data_files:
- split: epoch_0
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_0-*
- split: epoch_1
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_1-*
- split: epoch_2
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_2-*
- split: epoch_3
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_3-*
- split: epoch_4
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_4-*
- split: epoch_5
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_5-*
- split: epoch_6
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_6-*
- split: epoch_7
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_7-*
- split: epoch_8
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_8-*
- split: epoch_9
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_9-*
- split: epoch_10
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_10-*
- split: epoch_11
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_11-*
- split: epoch_12
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_12-*
- split: epoch_13
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_13-*
- split: epoch_14
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_14-*
- split: epoch_15
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_15-*
- split: epoch_16
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_16-*
- split: epoch_17
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_17-*
- split: epoch_18
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_18-*
- split: epoch_19
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_19-*
- split: epoch_20
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_20-*
- split: epoch_21
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_21-*
- split: epoch_22
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_22-*
- split: epoch_23
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_23-*
- split: epoch_24
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_24-*
- split: epoch_25
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_25-*
- split: epoch_26
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_26-*
- split: epoch_27
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_27-*
- split: epoch_28
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_28-*
- split: epoch_29
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_29-*
---
|
CyberHarem/flamebringer_arknights | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of flamebringer_arknights
This is the dataset of flamebringer_arknights, containing 166 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
| Name | Images | Download | Description |
|:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------|
| raw | 166 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 327 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 166 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 166 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 166 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 166 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 166 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 327 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 327 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 327 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
316usman/thematic4d-pw | ---
dataset_info:
features:
- name: text
dtype: string
- name: country
dtype: string
- name: document_url
dtype: string
- name: source_url
dtype: string
splits:
- name: train
num_bytes: 1562054967
num_examples: 2427353
download_size: 604248860
dataset_size: 1562054967
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
awacke1/LOINC-CodeSet-Value-Description.csv | ---
license: mit
---
LOINC-CodeSet-Value-Description.csv |
Tarmana/pendahuluan_jurnal_pertanian | ---
task_categories:
- summarization
language:
- id
size_categories:
- n<1K
--- |
Wgp/test | ---
license: apache-2.0
language:
- ch
tags:
- medical
- art
pretty_name: '1112312312312'
size_categories:
- 10K<n<100K
---
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
hongerzh/nft_prediction_1_with_dates | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
- name: label
dtype: float64
- name: time
dtype: float64
splits:
- name: train
num_bytes: 802026316.85
num_examples: 3495
- name: validation
num_bytes: 89427799.0
num_examples: 500
- name: test
num_bytes: 182630437.0
num_examples: 997
download_size: 856594702
dataset_size: 1074084552.85
---
# Dataset Card for "nft_prediction_1_with_dates"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
matlok/python-image-copilot-training-using-function-knowledge-graphs | ---
license:
- other
pretty_name: >-
python copilot image training using function knowledge graphs
dataset_info:
- config_name: view_schema
splits:
- name: view_schema
configs:
- config_name: view_schema
data_files:
- split: view_schema
path: files/lok-python-copilot-img.func-v1_00001364.parquet
size_categories:
- 100K<n<1M
tags:
- python-copilot
- python-coding
- python-architecture
- knowledge-graphs
- multimodal
- text-image-audio
- fine-tuning
- training
- question-answering
- image-knowledge-graph
- alpaca
- mp3
- png
- text
- instruct
- function
- functions
# supported task_categories
# text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, conversational, feature-extraction, text-generation, text2text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-retrieval, time-series-forecasting, text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, other
task_categories:
- text-to-image
- image-to-image
- question-answering
# supported task_ids
# acceptability-classification, entity-linking-classification, fact-checking, intent-classification, language-identification, multi-class-classification, multi-label-classification, multi-input-text-classification, natural-language-inference, semantic-similarity-classification, sentiment-classification, topic-classification, semantic-similarity-scoring, sentiment-scoring, sentiment-analysis, hate-speech-detection, text-scoring, named-entity-recognition, part-of-speech, parsing, lemmatization, word-sense-disambiguation, coreference-resolution, extractive-qa, open-domain-qa, closed-domain-qa, news-articles-summarization, news-articles-headline-generation, dialogue-generation, dialogue-modeling, language-modeling, text-simplification, explanation-generation, abstractive-qa, open-domain-abstractive-qa, closed-domain-qa, open-book-qa, closed-book-qa, slot-filling, masked-language-modeling, keyword-spotting, speaker-identification, audio-intent-classification, audio-emotion-recognition, audio-language-identification, multi-label-image-classification, multi-class-image-classification, face-detection, vehicle-detection, instance-segmentation, semantic-segmentation, panoptic-segmentation, image-captioning, image-inpainting, image-colorization, super-resolution, grasping, task-planning, tabular-multi-class-classification, tabular-multi-label-classification, tabular-single-column-regression, rdf-to-text, multiple-choice-qa, multiple-choice-coreference-resolution, document-retrieval, utterance-retrieval, entity-linking-retrieval, fact-checking-retrieval, univariate-time-series-forecasting, multivariate-time-series-forecasting, visual-question-answering, document-question-answering
task_ids:
- parsing
---
## Python Copilot Image Training using Function Knowledge Graphs
This dataset is a subset of the matlok python copilot datasets. Please refer to the [Multimodal Python Copilot Training Overview](https://huggingface.co/datasets/matlok/multimodal-python-copilot-training-overview) for more details on how to use this dataset.
### Details
Each row contains a png file in the **dbytes** column.
- Rows: 134357
- Size: 130.5 GB
- Data type: png
- Format: Knowledge graph using NetworkX with alpaca text box
### Schema
The png is in the **dbytes** column:
```
{
"dbytes": "binary",
"dbytes_len": "int64",
"dbytes_mb": "float64",
"filename": "string",
"path": "string",
"repo": "string",
"type": "string"
}
```
### How to use the dataset
```python
from datasets import load_dataset
ds = load_dataset("matlok/python-image-copilot-training-using-function-knowledge-graphs", data_dir="files")
```
|
worldboss/ghana-news | ---
language:
- en
license: apache-2.0
size_categories:
- 10K<n<100K
task_categories:
- conversational
- text-generation
- summarization
- question-answering
- text-classification
- text-retrieval
- translation
pretty_name: No Robots
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: title
dtype: string
- name: content
dtype: string
- name: author
dtype: string
- name: category
dtype: string
- name: published_date
dtype: string
- name: page_url
dtype: string
splits:
- name: train
num_bytes: 81377758
num_examples: 30374
- name: test
num_bytes: 12272769
num_examples: 9489
download_size: 0
dataset_size: 93650527
tags:
- ghana
- news
- politics
- science
- business
- ghana-news
---
### Description 🙅♂️🤖
GhanaNews dataset is a collection of news articles from various Ghanaian News Portals (MyJoyOnline, GraphicOnline, GhanaWeb, PulseGh, CitiNewsOnline, ect). The dataset is provided by the academic comunity for research purposes in data mining (clustering, classification, etc), information retrieval (ranking, search, etc), xml, data compression, data streaming, and any other non-commercial activity.
The Ghana news topic classification dataset is constructed by Theophilus Siameh (theodondre@gmail.com) from the dataset above.
### Context QA: in context question answering from an article
```shell
{"article": "...", "question": "...", "answer": "..."}
```
### Article and Summary
```shell
{"article": "...", "summary": "..."}
```
### Dataset Format
```shell
{
"title": "...",
"content": "...",
"author": "...",
"category": "...",
"published_date": "...",
"page_url": "..."
}
```
### Load Dataset
```shell
pip install datasets
```
```python
from datasets import load_dataset
train = load_dataset("worldboss/ghana-news", split="train")
test = load_dataset("worldboss/ghana-news", split="test")
pd.DataFrame(train).head()
``` |
shidowake/glaive-code-assistant-v1-sharegpt-format_split_17 | ---
dataset_info:
features:
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
splits:
- name: train
num_bytes: 10503837.603832223
num_examples: 6805
download_size: 5144075
dataset_size: 10503837.603832223
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Yannael/wikipedia-brezhoneg | ---
annotations_creators:
- no-annotation
language:
- br
language_creators:
- found
license:
- cc-by-sa-3.0
- gfdl
multilinguality:
- monolingual
pretty_name: Wikipedia Brezhoneg January 2024
size_categories:
- 10K<n<100K
source_datasets: []
tags:
- pretraining
- language modelling
- wikipedia
- web
task_ids: []
---
# Dataset Card for Wikipedia Brezhoneg January 2024
Dataset created using this [repo](https://huggingface.co/datasets/olm/wikipedia) with a January 2024 Wikipedia snapshot.
|
swag | ---
annotations_creators:
- crowdsourced
- machine-generated
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- natural-language-inference
paperswithcode_id: swag
pretty_name: Situations With Adversarial Generations
dataset_info:
- config_name: regular
features:
- name: video-id
dtype: string
- name: fold-ind
dtype: string
- name: startphrase
dtype: string
- name: sent1
dtype: string
- name: sent2
dtype: string
- name: gold-source
dtype: string
- name: ending0
dtype: string
- name: ending1
dtype: string
- name: ending2
dtype: string
- name: ending3
dtype: string
- name: label
dtype:
class_label:
names:
'0': '0'
'1': '1'
'2': '2'
'3': '3'
splits:
- name: train
num_bytes: 30274672
num_examples: 73546
- name: validation
num_bytes: 8451771
num_examples: 20006
- name: test
num_bytes: 8417644
num_examples: 20005
download_size: 43954806
dataset_size: 47144087
- config_name: full
features:
- name: video-id
dtype: string
- name: fold-ind
dtype: string
- name: startphrase
dtype: string
- name: gold-ending
dtype: string
- name: distractor-0
dtype: string
- name: distractor-1
dtype: string
- name: distractor-2
dtype: string
- name: distractor-3
dtype: string
- name: gold-source
dtype: string
- name: gold-type
dtype: string
- name: distractor-0-type
dtype: string
- name: distractor-1-type
dtype: string
- name: distractor-2-type
dtype: string
- name: distractor-3-type
dtype: string
- name: sent1
dtype: string
- name: sent2
dtype: string
splits:
- name: train
num_bytes: 34941649
num_examples: 73546
- name: validation
num_bytes: 9832603
num_examples: 20006
download_size: 40537624
dataset_size: 44774252
---
# Dataset Card for Situations With Adversarial Generations
## 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:** [SWAG AF](https://rowanzellers.com/swag/)
- **Repository:** [Github repository](https://github.com/rowanz/swagaf/tree/master/data)
- **Paper:** [SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference](https://arxiv.org/abs/1808.05326)
- **Leaderboard:** [SWAG Leaderboard](https://leaderboard.allenai.org/swag)
- **Point of Contact:** [Rowan Zellers](https://rowanzellers.com/#contact)
### Dataset Summary
Given a partial description like "she opened the hood of the car,"
humans can reason about the situation and anticipate what might come
next ("then, she examined the engine"). SWAG (Situations With Adversarial Generations)
is a large-scale dataset for this task of grounded commonsense
inference, unifying natural language inference and physically grounded reasoning.
The dataset consists of 113k multiple choice questions about grounded situations
(73k training, 20k validation, 20k test).
Each question is a video caption from LSMDC or ActivityNet Captions,
with four answer choices about what might happen next in the scene.
The correct answer is the (real) video caption for the next event in the video;
the three incorrect answers are adversarially generated and human verified,
so as to fool machines but not humans. SWAG aims to be a benchmark for
evaluating grounded commonsense NLI and for learning representations.
### Supported Tasks and Leaderboards
The dataset introduces the task of grounded commonsense inference, unifying natural language inference and commonsense reasoning.
### Languages
The text in the dataset is in English. The associated BCP-47 code is `en`.
## Dataset Structure
### Data Instances
The `regular` configuration should be used for modeling. An example looks like this:
```
{
"video-id": "anetv_dm5WXFiQZUQ",
"fold-ind": "18419",
"startphrase", "He rides the motorcycle down the hall and into the elevator. He",
"sent1": "He rides the motorcycle down the hall and into the elevator."
"sent2": "He",
"gold-source": "gold",
"ending0": "looks at a mirror in the mirror as he watches someone walk through a door.",
"ending1": "stops, listening to a cup of coffee with the seated woman, who's standing.",
"ending2": "exits the building and rides the motorcycle into a casino where he performs several tricks as people watch.",
"ending3": "pulls the bag out of his pocket and hands it to someone's grandma.",
"label": 2,
}
```
Note that the test are reseved for blind submission on the leaderboard.
The full train and validation sets provide more information regarding the collection process.
### Data Fields
- `video-id`: identification
- `fold-ind`: identification
- `startphrase`: the context to be filled
- `sent1`: the first sentence
- `sent2`: the start of the second sentence (to be filled)
- `gold-source`: generated or comes from the found completion
- `ending0`: first proposition
- `ending1`: second proposition
- `ending2`: third proposition
- `ending3`: fourth proposition
- `label`: the correct proposition
More info concerning the fields can be found [on the original repo](https://github.com/rowanz/swagaf/tree/master/data).
### Data Splits
The dataset consists of 113k multiple choice questions about grounded situations: 73k for training, 20k for validation, and 20k for (blind) test.
## Dataset Creation
### Curation Rationale
The authors seek dataset diversity while minimizing annotation artifacts, conditional stylistic patterns such as length and word-preference biases. To avoid introducing easily “gamed” patterns, they introduce Adversarial Filtering (AF), a generally- applicable treatment involving the iterative refinement of a set of assignments to increase the entropy under a chosen model family. The dataset is then human verified by paid crowdsourcers.
### Source Data
This section describes the source data (e.g. news text and headlines, social media posts, translated sentences,...)
#### Initial Data Collection and Normalization
The dataset is derived from pairs of consecutive video captions from [ActivityNet Captions](https://cs.stanford.edu/people/ranjaykrishna/densevid/) and the [Large Scale Movie Description Challenge](https://sites.google.com/site/describingmovies/). The two datasets are slightly different in nature and allow us to achieve broader coverage: ActivityNet contains 20k YouTube clips containing one of 203 activity types (such as doing gymnastics or playing guitar); LSMDC consists of 128k movie captions (audio descriptions and scripts).
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
Annotations are first machine generated and then adversarially filtered. Finally, the remaining examples are human-verified by paid crowdsourcers.
#### 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
Unknown
### Citation Information
```
@inproceedings{zellers2018swagaf,
title={SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference},
author={Zellers, Rowan and Bisk, Yonatan and Schwartz, Roy and Choi, Yejin},
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
year={2018}
}
```
### Contributions
Thanks to [@VictorSanh](https://github.com/VictorSanh) for adding this dataset. |
shrey9669/donut_trial | ---
dataset_info:
features:
- name: image
dtype: image
- name: ground_truth
dtype: string
splits:
- name: train
num_bytes: 1761988.0
num_examples: 3
- name: test
num_bytes: 424265.0
num_examples: 1
- name: validation
num_bytes: 429924.0
num_examples: 1
download_size: 1866897
dataset_size: 2616177.0
---
# Dataset Card for "donut_trial"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
duskvirkus/dafonts-free | ---
license: other
---
|
Vinisf/voz | ---
license: openrail
---
|
Tverous/SemEval-Audio | ---
dataset_info:
features:
- name: video_name
dtype: string
- name: audio
dtype: audio
- name: text
dtype: string
- name: speaker
dtype: string
- name: emotion
dtype:
class_label:
names:
'0': anger
'1': disgust
'2': fear
'3': joy
'4': neutral
'5': sadness
'6': surprise
splits:
- name: train
num_bytes: 684419162.647
num_examples: 13353
download_size: 695130678
dataset_size: 684419162.647
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "SemEval-Audio"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
nc33/multispan_singlespan_qa | ---
license: mit
---
|
ritwikraha/random-storage | ---
license: creativeml-openrail-m
---
|
haizad/cypherhackz-scraped | ---
language:
- en
---
* website: [cypherhackz](https://www.cypherhackz.net/)
* Number of pages scraped: 9
* Number of posts scraped: 805
* Link to dataset on [Huggingface](https://huggingface.co/datasets/haizad/cypherhackz-scraped) |
open-llm-leaderboard/details_abhishek__llama2guanacotest | ---
pretty_name: Evaluation run of abhishek/llama2guanacotest
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [abhishek/llama2guanacotest](https://huggingface.co/abhishek/llama2guanacotest)\
\ 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_abhishek__llama2guanacotest\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-22T17:34:42.809014](https://huggingface.co/datasets/open-llm-leaderboard/details_abhishek__llama2guanacotest/blob/main/results_2023-09-22T17-34-42.809014.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\": 0.1018246644295302,\n\
\ \"em_stderr\": 0.0030970392367407284,\n \"f1\": 0.15182571308724796,\n\
\ \"f1_stderr\": 0.0032356577343186617,\n \"acc\": 0.42458141676534983,\n\
\ \"acc_stderr\": 0.010661835808025592\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.1018246644295302,\n \"em_stderr\": 0.0030970392367407284,\n\
\ \"f1\": 0.15182571308724796,\n \"f1_stderr\": 0.0032356577343186617\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.11751326762699014,\n \
\ \"acc_stderr\": 0.008870331256489988\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7316495659037096,\n \"acc_stderr\": 0.012453340359561195\n\
\ }\n}\n```"
repo_url: https://huggingface.co/abhishek/llama2guanacotest
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_17T13_26_15.590917
path:
- '**/details_harness|arc:challenge|25_2023-08-17T13:26:15.590917.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-17T13:26:15.590917.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_22T17_34_42.809014
path:
- '**/details_harness|drop|3_2023-09-22T17-34-42.809014.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-22T17-34-42.809014.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_22T17_34_42.809014
path:
- '**/details_harness|gsm8k|5_2023-09-22T17-34-42.809014.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-22T17-34-42.809014.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_17T13_26_15.590917
path:
- '**/details_harness|hellaswag|10_2023-08-17T13:26:15.590917.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-17T13:26:15.590917.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_17T13_26_15.590917
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-17T13:26:15.590917.parquet'
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- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-17T13:26:15.590917.parquet'
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- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-17T13:26:15.590917.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-17T13:26:15.590917.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-17T13:26:15.590917.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_17T13_26_15.590917
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-17T13:26:15.590917.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-17T13:26:15.590917.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_17T13_26_15.590917
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-17T13:26:15.590917.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-17T13:26:15.590917.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_17T13_26_15.590917
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-17T13:26:15.590917.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-17T13:26:15.590917.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_17T13_26_15.590917
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-17T13:26:15.590917.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-17T13:26:15.590917.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_17T13_26_15.590917
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-17T13:26:15.590917.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-17T13:26:15.590917.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_17T13_26_15.590917
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-17T13:26:15.590917.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-17T13:26:15.590917.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_17T13_26_15.590917
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-17T13:26:15.590917.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-17T13:26:15.590917.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_17T13_26_15.590917
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-17T13:26:15.590917.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-17T13:26:15.590917.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_17T13_26_15.590917
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-17T13:26:15.590917.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-17T13:26:15.590917.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_17T13_26_15.590917
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-17T13:26:15.590917.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-17T13:26:15.590917.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_17T13_26_15.590917
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-17T13:26:15.590917.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-17T13:26:15.590917.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_17T13_26_15.590917
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-17T13:26:15.590917.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-17T13:26:15.590917.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_17T13_26_15.590917
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-17T13:26:15.590917.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-17T13:26:15.590917.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_17T13_26_15.590917
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-17T13:26:15.590917.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-17T13:26:15.590917.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_17T13_26_15.590917
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-17T13:26:15.590917.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-17T13:26:15.590917.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_17T13_26_15.590917
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-17T13:26:15.590917.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-17T13:26:15.590917.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_17T13_26_15.590917
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-17T13:26:15.590917.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-17T13:26:15.590917.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_17T13_26_15.590917
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-17T13:26:15.590917.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-17T13:26:15.590917.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_17T13_26_15.590917
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-17T13:26:15.590917.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-17T13:26:15.590917.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_17T13_26_15.590917
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-17T13:26:15.590917.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-17T13:26:15.590917.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_17T13_26_15.590917
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-17T13:26:15.590917.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-17T13:26:15.590917.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_17T13_26_15.590917
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-17T13:26:15.590917.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-17T13:26:15.590917.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_17T13_26_15.590917
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-17T13:26:15.590917.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-17T13:26:15.590917.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_17T13_26_15.590917
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-17T13:26:15.590917.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-17T13:26:15.590917.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_17T13_26_15.590917
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-17T13:26:15.590917.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-17T13:26:15.590917.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_08_17T13_26_15.590917
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-17T13:26:15.590917.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-17T13:26:15.590917.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_17T13_26_15.590917
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-17T13:26:15.590917.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-17T13:26:15.590917.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_08_17T13_26_15.590917
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-17T13:26:15.590917.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-17T13:26:15.590917.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_17T13_26_15.590917
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-17T13:26:15.590917.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-17T13:26:15.590917.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_17T13_26_15.590917
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-17T13:26:15.590917.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-17T13:26:15.590917.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_17T13_26_15.590917
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-17T13:26:15.590917.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-17T13:26:15.590917.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_17T13_26_15.590917
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-17T13:26:15.590917.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-17T13:26:15.590917.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_17T13_26_15.590917
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-17T13:26:15.590917.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-17T13:26:15.590917.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_17T13_26_15.590917
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-17T13:26:15.590917.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-17T13:26:15.590917.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_17T13_26_15.590917
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-17T13:26:15.590917.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-17T13:26:15.590917.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_17T13_26_15.590917
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-17T13:26:15.590917.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-17T13:26:15.590917.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_17T13_26_15.590917
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-17T13:26:15.590917.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-17T13:26:15.590917.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_08_17T13_26_15.590917
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-17T13:26:15.590917.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-17T13:26:15.590917.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_08_17T13_26_15.590917
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-17T13:26:15.590917.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-17T13:26:15.590917.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_17T13_26_15.590917
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-17T13:26:15.590917.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-17T13:26:15.590917.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_17T13_26_15.590917
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-17T13:26:15.590917.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-17T13:26:15.590917.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_17T13_26_15.590917
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-17T13:26:15.590917.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-17T13:26:15.590917.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_17T13_26_15.590917
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-17T13:26:15.590917.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-17T13:26:15.590917.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_17T13_26_15.590917
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-17T13:26:15.590917.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-17T13:26:15.590917.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_17T13_26_15.590917
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-17T13:26:15.590917.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-17T13:26:15.590917.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_17T13_26_15.590917
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-17T13:26:15.590917.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-17T13:26:15.590917.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_17T13_26_15.590917
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-17T13:26:15.590917.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-17T13:26:15.590917.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_17T13_26_15.590917
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-17T13:26:15.590917.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-17T13:26:15.590917.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_17T13_26_15.590917
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-17T13:26:15.590917.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-17T13:26:15.590917.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_17T13_26_15.590917
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-17T13:26:15.590917.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-17T13:26:15.590917.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_17T13_26_15.590917
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-17T13:26:15.590917.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-17T13:26:15.590917.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_17T13_26_15.590917
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-17T13:26:15.590917.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-17T13:26:15.590917.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_17T13_26_15.590917
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-17T13:26:15.590917.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-17T13:26:15.590917.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_17T13_26_15.590917
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-17T13:26:15.590917.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-17T13:26:15.590917.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_17T13_26_15.590917
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-17T13:26:15.590917.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-17T13:26:15.590917.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_17T13_26_15.590917
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-17T13:26:15.590917.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-17T13:26:15.590917.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_17T13_26_15.590917
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-17T13:26:15.590917.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-17T13:26:15.590917.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_17T13_26_15.590917
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-17T13:26:15.590917.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-17T13:26:15.590917.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_22T17_34_42.809014
path:
- '**/details_harness|winogrande|5_2023-09-22T17-34-42.809014.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-22T17-34-42.809014.parquet'
- config_name: results
data_files:
- split: 2023_08_17T13_26_15.590917
path:
- results_2023-08-17T13:26:15.590917.parquet
- split: 2023_09_22T17_34_42.809014
path:
- results_2023-09-22T17-34-42.809014.parquet
- split: latest
path:
- results_2023-09-22T17-34-42.809014.parquet
---
# Dataset Card for Evaluation run of abhishek/llama2guanacotest
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/abhishek/llama2guanacotest
- **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 [abhishek/llama2guanacotest](https://huggingface.co/abhishek/llama2guanacotest) 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_abhishek__llama2guanacotest",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-22T17:34:42.809014](https://huggingface.co/datasets/open-llm-leaderboard/details_abhishek__llama2guanacotest/blob/main/results_2023-09-22T17-34-42.809014.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": 0.1018246644295302,
"em_stderr": 0.0030970392367407284,
"f1": 0.15182571308724796,
"f1_stderr": 0.0032356577343186617,
"acc": 0.42458141676534983,
"acc_stderr": 0.010661835808025592
},
"harness|drop|3": {
"em": 0.1018246644295302,
"em_stderr": 0.0030970392367407284,
"f1": 0.15182571308724796,
"f1_stderr": 0.0032356577343186617
},
"harness|gsm8k|5": {
"acc": 0.11751326762699014,
"acc_stderr": 0.008870331256489988
},
"harness|winogrande|5": {
"acc": 0.7316495659037096,
"acc_stderr": 0.012453340359561195
}
}
```
### 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] |
Dzeniks/wikipedia_keywords | ---
license: apache-2.0
---
|
giuid/qrecc_with_context_and_answers | ---
task_categories:
- conversational
- question-answering
language:
- en
---
This is the QRECC dataset arranged to be used for a query rewriting task.
Each line is composed as follow:
INTRODUCTION token is followed by the PREVIOUS TURNS of the conversation together WITH THE ANSWERS provided in the dataset
QUESTION token is followed by the current query the system should rewrite
ANSWER token is followed by the rewriting of the current query
|
DavidLanz/roleplay_chat_instruction | ---
license: cc-by-sa-4.0
---
|
ALI-B/Python_testing | ---
license: llama2
---
|
elsatch/datos-leyes-civiles-peruanas-v2 | ---
task_categories:
- question-answering
language:
- es
tags:
- legal
- peruvian
size_categories:
- n<1K
---
# Datos Leyes Civiles Peruanas v2
Este dataset es una variante de [SrAlex/datos-leyes-civiles-peruanas-v2](https://huggingface.co/datasets/SrAlex/datos-leyes-civiles-peruanas-v2/).
El dataset original presentaba un prompt en formato prompt, que incluia las preguntas y respuestas dentro de una misma columna:
```
<s>[INST] Eres un experto en leyes peruanas, dime el significado de Jurista[/INST] El significado de Jurista es Se dice de quién es versado en la ciencia del derecho, es el que se dedica a la resolución de las dudas o consultas jurídicas. Es el agente de la doctrina, según el profesor Víctor García Toma. </s>
```
Este nuevo dataset se ha generado dividiendo las instrucciones y las respuestas en columnas diferentes. A continuación se han convertido las instrucciones en preguntas, utilizando open mixtral 8x7b, y se han almancenado un una nueva columna. Por último, se ha añadido una nueva columna de system_prompt. El nombre de las columnas se ha adecuado para coincidir con el de los datasets de OpenOrca, de cara a facilitar la interoperabilidad.
Nota: El dataset original no incluye ningún tipo de información sobre licencias, por lo que esta variante tampoco la incluye. Se intentará aclarar la licencia del fichero original para adaptar la de este dataset de forma acorde.
## English version
This dataset is a variant of [SrAlex/datos-leyes-civiles-peruanas-v2](https://huggingface.co/datasets/SrAlex/datos-leyes-civiles-peruanas-v2/).
The original dataset presented a prompt in prompt format, which included the questions and answers within the same column:
```
<s>[INST] Eres un experto en leyes peruanas, dime el significado de Jurista[/INST] El significado de Jurista es Se dice de quién es versado en la ciencia del derecho, es el que se dedica a la resolución de las dudas o consultas jurídicas. Es el agente de la doctrina, según el profesor Víctor García Toma. </s>
```
This new dataset has been generated by dividing the instructions and responses into different columns. The instructions have then been converted into questions using open mixtral 8x7b and stored in a new column. Finally, a new system_prompt column has been added. The name of the columns has been adapted to match that of the OpenOrca datasets, in order to facilitate interoperability.
|
autoevaluate/autoeval-staging-eval-project-squad-95d5e1fd-11835578 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad
eval_info:
task: extractive_question_answering
model: mbartolo/electra-large-synqa
metrics: []
dataset_name: squad
dataset_config: plain_text
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# 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: mbartolo/electra-large-synqa
* Dataset: squad
* Config: plain_text
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mbartolo ](https://huggingface.co/mbartolo ) for evaluating this model. |
open-llm-leaderboard/details_BarraHome__zephyr-dpo-v2 | ---
pretty_name: Evaluation run of BarraHome/zephyr-dpo-v2
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [BarraHome/zephyr-dpo-v2](https://huggingface.co/BarraHome/zephyr-dpo-v2) on the\
\ [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_BarraHome__zephyr-dpo-v2\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-02-04T08:58:25.311637](https://huggingface.co/datasets/open-llm-leaderboard/details_BarraHome__zephyr-dpo-v2/blob/main/results_2024-02-04T08-58-25.311637.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\": 0.5842601909932569,\n\
\ \"acc_stderr\": 0.033311942698808106,\n \"acc_norm\": 0.5900864037887772,\n\
\ \"acc_norm_stderr\": 0.03399934210645472,\n \"mc1\": 0.3708690330477356,\n\
\ \"mc1_stderr\": 0.01690969358024882,\n \"mc2\": 0.5616226683920723,\n\
\ \"mc2_stderr\": 0.015980395758532336\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5477815699658704,\n \"acc_stderr\": 0.014544519880633827,\n\
\ \"acc_norm\": 0.5784982935153583,\n \"acc_norm_stderr\": 0.014430197069326023\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6367257518422625,\n\
\ \"acc_stderr\": 0.004799599840397376,\n \"acc_norm\": 0.8272256522605059,\n\
\ \"acc_norm_stderr\": 0.0037727944471851503\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \
\ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5407407407407407,\n\
\ \"acc_stderr\": 0.04304979692464242,\n \"acc_norm\": 0.5407407407407407,\n\
\ \"acc_norm_stderr\": 0.04304979692464242\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.618421052631579,\n \"acc_stderr\": 0.03953173377749194,\n\
\ \"acc_norm\": 0.618421052631579,\n \"acc_norm_stderr\": 0.03953173377749194\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.62,\n\
\ \"acc_stderr\": 0.048783173121456316,\n \"acc_norm\": 0.62,\n \
\ \"acc_norm_stderr\": 0.048783173121456316\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6264150943396226,\n \"acc_stderr\": 0.029773082713319875,\n\
\ \"acc_norm\": 0.6264150943396226,\n \"acc_norm_stderr\": 0.029773082713319875\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6805555555555556,\n\
\ \"acc_stderr\": 0.038990736873573344,\n \"acc_norm\": 0.6805555555555556,\n\
\ \"acc_norm_stderr\": 0.038990736873573344\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.4,\n \"acc_stderr\": 0.04923659639173309,\n \
\ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.04923659639173309\n },\n\
\ \"harness|hendrycksTest-college_computer_science|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_mathematics|5\"\
: {\n \"acc\": 0.22,\n \"acc_stderr\": 0.04163331998932269,\n \
\ \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.04163331998932269\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.630057803468208,\n\
\ \"acc_stderr\": 0.036812296333943194,\n \"acc_norm\": 0.630057803468208,\n\
\ \"acc_norm_stderr\": 0.036812296333943194\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.4117647058823529,\n \"acc_stderr\": 0.04897104952726366,\n\
\ \"acc_norm\": 0.4117647058823529,\n \"acc_norm_stderr\": 0.04897104952726366\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.67,\n \"acc_stderr\": 0.047258156262526094,\n \"acc_norm\": 0.67,\n\
\ \"acc_norm_stderr\": 0.047258156262526094\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.48936170212765956,\n \"acc_stderr\": 0.03267862331014063,\n\
\ \"acc_norm\": 0.48936170212765956,\n \"acc_norm_stderr\": 0.03267862331014063\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.41228070175438597,\n\
\ \"acc_stderr\": 0.04630653203366595,\n \"acc_norm\": 0.41228070175438597,\n\
\ \"acc_norm_stderr\": 0.04630653203366595\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5172413793103449,\n \"acc_stderr\": 0.04164188720169375,\n\
\ \"acc_norm\": 0.5172413793103449,\n \"acc_norm_stderr\": 0.04164188720169375\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.3968253968253968,\n \"acc_stderr\": 0.025197101074246487,\n \"\
acc_norm\": 0.3968253968253968,\n \"acc_norm_stderr\": 0.025197101074246487\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.42857142857142855,\n\
\ \"acc_stderr\": 0.0442626668137991,\n \"acc_norm\": 0.42857142857142855,\n\
\ \"acc_norm_stderr\": 0.0442626668137991\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.33,\n \"acc_stderr\": 0.047258156262526045,\n \
\ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.047258156262526045\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.7387096774193549,\n \"acc_stderr\": 0.024993053397764812,\n \"\
acc_norm\": 0.7387096774193549,\n \"acc_norm_stderr\": 0.024993053397764812\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.5123152709359606,\n \"acc_stderr\": 0.035169204442208966,\n \"\
acc_norm\": 0.5123152709359606,\n \"acc_norm_stderr\": 0.035169204442208966\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.62,\n \"acc_stderr\": 0.04878317312145632,\n \"acc_norm\"\
: 0.62,\n \"acc_norm_stderr\": 0.04878317312145632\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7272727272727273,\n \"acc_stderr\": 0.0347769116216366,\n\
\ \"acc_norm\": 0.7272727272727273,\n \"acc_norm_stderr\": 0.0347769116216366\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7525252525252525,\n \"acc_stderr\": 0.030746300742124495,\n \"\
acc_norm\": 0.7525252525252525,\n \"acc_norm_stderr\": 0.030746300742124495\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.7927461139896373,\n \"acc_stderr\": 0.029252823291803638,\n\
\ \"acc_norm\": 0.7927461139896373,\n \"acc_norm_stderr\": 0.029252823291803638\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.5538461538461539,\n \"acc_stderr\": 0.02520357177302833,\n \
\ \"acc_norm\": 0.5538461538461539,\n \"acc_norm_stderr\": 0.02520357177302833\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.2518518518518518,\n \"acc_stderr\": 0.026466117538959916,\n \
\ \"acc_norm\": 0.2518518518518518,\n \"acc_norm_stderr\": 0.026466117538959916\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.5966386554621849,\n \"acc_stderr\": 0.03186608121408832,\n \
\ \"acc_norm\": 0.5966386554621849,\n \"acc_norm_stderr\": 0.03186608121408832\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.33774834437086093,\n \"acc_stderr\": 0.03861557546255169,\n \"\
acc_norm\": 0.33774834437086093,\n \"acc_norm_stderr\": 0.03861557546255169\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.7798165137614679,\n \"acc_stderr\": 0.01776597865232756,\n \"\
acc_norm\": 0.7798165137614679,\n \"acc_norm_stderr\": 0.01776597865232756\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.4305555555555556,\n \"acc_stderr\": 0.033769221512523366,\n \"\
acc_norm\": 0.4305555555555556,\n \"acc_norm_stderr\": 0.033769221512523366\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.75,\n \"acc_stderr\": 0.03039153369274154,\n \"acc_norm\": 0.75,\n\
\ \"acc_norm_stderr\": 0.03039153369274154\n },\n \"harness|hendrycksTest-high_school_world_history|5\"\
: {\n \"acc\": 0.7341772151898734,\n \"acc_stderr\": 0.02875679962965834,\n\
\ \"acc_norm\": 0.7341772151898734,\n \"acc_norm_stderr\": 0.02875679962965834\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6412556053811659,\n\
\ \"acc_stderr\": 0.03219079200419995,\n \"acc_norm\": 0.6412556053811659,\n\
\ \"acc_norm_stderr\": 0.03219079200419995\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.6870229007633588,\n \"acc_stderr\": 0.04066962905677697,\n\
\ \"acc_norm\": 0.6870229007633588,\n \"acc_norm_stderr\": 0.04066962905677697\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.8016528925619835,\n \"acc_stderr\": 0.03640118271990947,\n \"\
acc_norm\": 0.8016528925619835,\n \"acc_norm_stderr\": 0.03640118271990947\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7222222222222222,\n\
\ \"acc_stderr\": 0.043300437496507437,\n \"acc_norm\": 0.7222222222222222,\n\
\ \"acc_norm_stderr\": 0.043300437496507437\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.6748466257668712,\n \"acc_stderr\": 0.03680350371286461,\n\
\ \"acc_norm\": 0.6748466257668712,\n \"acc_norm_stderr\": 0.03680350371286461\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4017857142857143,\n\
\ \"acc_stderr\": 0.046533331469736455,\n \"acc_norm\": 0.4017857142857143,\n\
\ \"acc_norm_stderr\": 0.046533331469736455\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.6990291262135923,\n \"acc_stderr\": 0.04541609446503949,\n\
\ \"acc_norm\": 0.6990291262135923,\n \"acc_norm_stderr\": 0.04541609446503949\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8632478632478633,\n\
\ \"acc_stderr\": 0.022509033937077805,\n \"acc_norm\": 0.8632478632478633,\n\
\ \"acc_norm_stderr\": 0.022509033937077805\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.68,\n \"acc_stderr\": 0.046882617226215034,\n \
\ \"acc_norm\": 0.68,\n \"acc_norm_stderr\": 0.046882617226215034\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7752234993614304,\n\
\ \"acc_stderr\": 0.014927447101937153,\n \"acc_norm\": 0.7752234993614304,\n\
\ \"acc_norm_stderr\": 0.014927447101937153\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.638728323699422,\n \"acc_stderr\": 0.025862201852277892,\n\
\ \"acc_norm\": 0.638728323699422,\n \"acc_norm_stderr\": 0.025862201852277892\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2670391061452514,\n\
\ \"acc_stderr\": 0.014796502622562557,\n \"acc_norm\": 0.2670391061452514,\n\
\ \"acc_norm_stderr\": 0.014796502622562557\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.6830065359477124,\n \"acc_stderr\": 0.02664327847450875,\n\
\ \"acc_norm\": 0.6830065359477124,\n \"acc_norm_stderr\": 0.02664327847450875\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6720257234726688,\n\
\ \"acc_stderr\": 0.026664410886937617,\n \"acc_norm\": 0.6720257234726688,\n\
\ \"acc_norm_stderr\": 0.026664410886937617\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.6450617283950617,\n \"acc_stderr\": 0.026624152478845853,\n\
\ \"acc_norm\": 0.6450617283950617,\n \"acc_norm_stderr\": 0.026624152478845853\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.46808510638297873,\n \"acc_stderr\": 0.029766675075873866,\n \
\ \"acc_norm\": 0.46808510638297873,\n \"acc_norm_stderr\": 0.029766675075873866\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.423728813559322,\n\
\ \"acc_stderr\": 0.012620785155885996,\n \"acc_norm\": 0.423728813559322,\n\
\ \"acc_norm_stderr\": 0.012620785155885996\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.5808823529411765,\n \"acc_stderr\": 0.029972807170464622,\n\
\ \"acc_norm\": 0.5808823529411765,\n \"acc_norm_stderr\": 0.029972807170464622\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6127450980392157,\n \"acc_stderr\": 0.019706875804085637,\n \
\ \"acc_norm\": 0.6127450980392157,\n \"acc_norm_stderr\": 0.019706875804085637\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\": 0.6244897959183674,\n \"acc_stderr\": 0.03100120903989484,\n\
\ \"acc_norm\": 0.6244897959183674,\n \"acc_norm_stderr\": 0.03100120903989484\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.746268656716418,\n\
\ \"acc_stderr\": 0.030769444967296014,\n \"acc_norm\": 0.746268656716418,\n\
\ \"acc_norm_stderr\": 0.030769444967296014\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.8,\n \"acc_stderr\": 0.04020151261036844,\n \
\ \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.04020151261036844\n },\n\
\ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.4759036144578313,\n\
\ \"acc_stderr\": 0.038879718495972646,\n \"acc_norm\": 0.4759036144578313,\n\
\ \"acc_norm_stderr\": 0.038879718495972646\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8128654970760234,\n \"acc_stderr\": 0.029913127232368036,\n\
\ \"acc_norm\": 0.8128654970760234,\n \"acc_norm_stderr\": 0.029913127232368036\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3708690330477356,\n\
\ \"mc1_stderr\": 0.01690969358024882,\n \"mc2\": 0.5616226683920723,\n\
\ \"mc2_stderr\": 0.015980395758532336\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7434885556432518,\n \"acc_stderr\": 0.012273648008759989\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.3025018953752843,\n \
\ \"acc_stderr\": 0.012652544133186141\n }\n}\n```"
repo_url: https://huggingface.co/BarraHome/zephyr-dpo-v2
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_04T08_57_54.838918
path:
- '**/details_harness|arc:challenge|25_2024-02-04T08-57-54.838918.parquet'
- split: 2024_02_04T08_58_25.311637
path:
- '**/details_harness|arc:challenge|25_2024-02-04T08-58-25.311637.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-02-04T08-58-25.311637.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_02_04T08_57_54.838918
path:
- '**/details_harness|gsm8k|5_2024-02-04T08-57-54.838918.parquet'
- split: 2024_02_04T08_58_25.311637
path:
- '**/details_harness|gsm8k|5_2024-02-04T08-58-25.311637.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-02-04T08-58-25.311637.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_02_04T08_57_54.838918
path:
- '**/details_harness|hellaswag|10_2024-02-04T08-57-54.838918.parquet'
- split: 2024_02_04T08_58_25.311637
path:
- '**/details_harness|hellaswag|10_2024-02-04T08-58-25.311637.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-02-04T08-58-25.311637.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_02_04T08_57_54.838918
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-04T08-57-54.838918.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-04T08-57-54.838918.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-04T08-57-54.838918.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-04T08-57-54.838918.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-04T08-57-54.838918.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-04T08-57-54.838918.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-04T08-57-54.838918.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-04T08-57-54.838918.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-04T08-57-54.838918.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-04T08-57-54.838918.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-04T08-57-54.838918.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-04T08-57-54.838918.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-04T08-57-54.838918.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-04T08-57-54.838918.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-04T08-57-54.838918.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-04T08-57-54.838918.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-04T08-57-54.838918.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-04T08-57-54.838918.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-04T08-57-54.838918.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-04T08-57-54.838918.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-04T08-57-54.838918.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-04T08-57-54.838918.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-04T08-57-54.838918.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-04T08-57-54.838918.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-04T08-57-54.838918.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-04T08-57-54.838918.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-04T08-57-54.838918.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-04T08-57-54.838918.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-04T08-57-54.838918.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-04T08-57-54.838918.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-04T08-57-54.838918.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-04T08-57-54.838918.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-04T08-57-54.838918.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-04T08-57-54.838918.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-04T08-57-54.838918.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-04T08-57-54.838918.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-04T08-57-54.838918.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-04T08-57-54.838918.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-04T08-57-54.838918.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-04T08-57-54.838918.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-04T08-57-54.838918.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-04T08-57-54.838918.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-04T08-57-54.838918.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-04T08-57-54.838918.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-04T08-57-54.838918.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-04T08-57-54.838918.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-04T08-57-54.838918.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-04T08-57-54.838918.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-04T08-57-54.838918.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-04T08-57-54.838918.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-04T08-57-54.838918.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-04T08-57-54.838918.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-04T08-57-54.838918.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-04T08-57-54.838918.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-04T08-57-54.838918.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-04T08-57-54.838918.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-04T08-57-54.838918.parquet'
- split: 2024_02_04T08_58_25.311637
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-04T08-58-25.311637.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-04T08-58-25.311637.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-04T08-58-25.311637.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_02_04T08_57_54.838918
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-04T08-57-54.838918.parquet'
- split: 2024_02_04T08_58_25.311637
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-04T08-58-25.311637.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-04T08-58-25.311637.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_02_04T08_57_54.838918
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-04T08-57-54.838918.parquet'
- split: 2024_02_04T08_58_25.311637
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-04T08-58-25.311637.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-04T08-58-25.311637.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_02_04T08_57_54.838918
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-04T08-57-54.838918.parquet'
- split: 2024_02_04T08_58_25.311637
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-04T08-58-25.311637.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-04T08-58-25.311637.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_02_04T08_57_54.838918
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-04T08-57-54.838918.parquet'
- split: 2024_02_04T08_58_25.311637
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-04T08-58-25.311637.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-04T08-58-25.311637.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_02_04T08_57_54.838918
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-04T08-57-54.838918.parquet'
- split: 2024_02_04T08_58_25.311637
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-04T08-58-25.311637.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-04T08-58-25.311637.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_02_04T08_57_54.838918
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-04T08-57-54.838918.parquet'
- split: 2024_02_04T08_58_25.311637
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-04T08-58-25.311637.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-04T08-58-25.311637.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_02_04T08_57_54.838918
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-04T08-57-54.838918.parquet'
- split: 2024_02_04T08_58_25.311637
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-04T08-58-25.311637.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-04T08-58-25.311637.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_02_04T08_57_54.838918
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-04T08-57-54.838918.parquet'
- split: 2024_02_04T08_58_25.311637
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-04T08-58-25.311637.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-04T08-58-25.311637.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_02_04T08_57_54.838918
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-04T08-57-54.838918.parquet'
- split: 2024_02_04T08_58_25.311637
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-04T08-58-25.311637.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-04T08-58-25.311637.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_02_04T08_57_54.838918
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-04T08-57-54.838918.parquet'
- split: 2024_02_04T08_58_25.311637
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-04T08-58-25.311637.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-04T08-58-25.311637.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_02_04T08_57_54.838918
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-04T08-57-54.838918.parquet'
- split: 2024_02_04T08_58_25.311637
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-04T08-58-25.311637.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-04T08-58-25.311637.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_02_04T08_57_54.838918
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-04T08-57-54.838918.parquet'
- split: 2024_02_04T08_58_25.311637
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-04T08-58-25.311637.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-04T08-58-25.311637.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_02_04T08_57_54.838918
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-04T08-57-54.838918.parquet'
- split: 2024_02_04T08_58_25.311637
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-04T08-58-25.311637.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-04T08-58-25.311637.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_02_04T08_57_54.838918
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-04T08-57-54.838918.parquet'
- split: 2024_02_04T08_58_25.311637
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-04T08-58-25.311637.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-04T08-58-25.311637.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_02_04T08_57_54.838918
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-04T08-57-54.838918.parquet'
- split: 2024_02_04T08_58_25.311637
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-04T08-58-25.311637.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-04T08-58-25.311637.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_02_04T08_57_54.838918
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-04T08-57-54.838918.parquet'
- split: 2024_02_04T08_58_25.311637
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-04T08-58-25.311637.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-04T08-58-25.311637.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_02_04T08_57_54.838918
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-04T08-57-54.838918.parquet'
- split: 2024_02_04T08_58_25.311637
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-04T08-58-25.311637.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-04T08-58-25.311637.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_02_04T08_57_54.838918
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-04T08-57-54.838918.parquet'
- split: 2024_02_04T08_58_25.311637
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-04T08-58-25.311637.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-04T08-58-25.311637.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_02_04T08_57_54.838918
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-04T08-57-54.838918.parquet'
- split: 2024_02_04T08_58_25.311637
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-04T08-58-25.311637.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-04T08-58-25.311637.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_02_04T08_57_54.838918
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-04T08-57-54.838918.parquet'
- split: 2024_02_04T08_58_25.311637
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-04T08-58-25.311637.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-04T08-58-25.311637.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_02_04T08_57_54.838918
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-04T08-57-54.838918.parquet'
- split: 2024_02_04T08_58_25.311637
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-04T08-58-25.311637.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-04T08-58-25.311637.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_02_04T08_57_54.838918
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-04T08-57-54.838918.parquet'
- split: 2024_02_04T08_58_25.311637
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-04T08-58-25.311637.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-04T08-58-25.311637.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_02_04T08_57_54.838918
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-04T08-57-54.838918.parquet'
- split: 2024_02_04T08_58_25.311637
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-04T08-58-25.311637.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-04T08-58-25.311637.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_02_04T08_57_54.838918
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-04T08-57-54.838918.parquet'
- split: 2024_02_04T08_58_25.311637
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-04T08-58-25.311637.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-04T08-58-25.311637.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_02_04T08_57_54.838918
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-04T08-57-54.838918.parquet'
- split: 2024_02_04T08_58_25.311637
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-04T08-58-25.311637.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-04T08-58-25.311637.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_02_04T08_57_54.838918
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-04T08-57-54.838918.parquet'
- split: 2024_02_04T08_58_25.311637
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-04T08-58-25.311637.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-04T08-58-25.311637.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_02_04T08_57_54.838918
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-04T08-57-54.838918.parquet'
- split: 2024_02_04T08_58_25.311637
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-04T08-58-25.311637.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-04T08-58-25.311637.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_02_04T08_57_54.838918
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-04T08-57-54.838918.parquet'
- split: 2024_02_04T08_58_25.311637
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-04T08-58-25.311637.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-04T08-58-25.311637.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_02_04T08_57_54.838918
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-04T08-57-54.838918.parquet'
- split: 2024_02_04T08_58_25.311637
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-04T08-58-25.311637.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-04T08-58-25.311637.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_02_04T08_57_54.838918
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-04T08-57-54.838918.parquet'
- split: 2024_02_04T08_58_25.311637
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-04T08-58-25.311637.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-04T08-58-25.311637.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_02_04T08_57_54.838918
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-04T08-57-54.838918.parquet'
- split: 2024_02_04T08_58_25.311637
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-04T08-58-25.311637.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-04T08-58-25.311637.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_02_04T08_57_54.838918
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-04T08-57-54.838918.parquet'
- split: 2024_02_04T08_58_25.311637
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-04T08-58-25.311637.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-04T08-58-25.311637.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_02_04T08_57_54.838918
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-04T08-57-54.838918.parquet'
- split: 2024_02_04T08_58_25.311637
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-04T08-58-25.311637.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-04T08-58-25.311637.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_02_04T08_57_54.838918
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-04T08-57-54.838918.parquet'
- split: 2024_02_04T08_58_25.311637
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-04T08-58-25.311637.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-04T08-58-25.311637.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_02_04T08_57_54.838918
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-04T08-57-54.838918.parquet'
- split: 2024_02_04T08_58_25.311637
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-04T08-58-25.311637.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-04T08-58-25.311637.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_02_04T08_57_54.838918
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-04T08-57-54.838918.parquet'
- split: 2024_02_04T08_58_25.311637
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-04T08-58-25.311637.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-04T08-58-25.311637.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_02_04T08_57_54.838918
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-04T08-57-54.838918.parquet'
- split: 2024_02_04T08_58_25.311637
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-04T08-58-25.311637.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-04T08-58-25.311637.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_02_04T08_57_54.838918
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-04T08-57-54.838918.parquet'
- split: 2024_02_04T08_58_25.311637
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-04T08-58-25.311637.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-04T08-58-25.311637.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_02_04T08_57_54.838918
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-04T08-57-54.838918.parquet'
- split: 2024_02_04T08_58_25.311637
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-04T08-58-25.311637.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-04T08-58-25.311637.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_02_04T08_57_54.838918
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-04T08-57-54.838918.parquet'
- split: 2024_02_04T08_58_25.311637
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-04T08-58-25.311637.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-04T08-58-25.311637.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_02_04T08_57_54.838918
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-04T08-57-54.838918.parquet'
- split: 2024_02_04T08_58_25.311637
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-04T08-58-25.311637.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-04T08-58-25.311637.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_02_04T08_57_54.838918
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-04T08-57-54.838918.parquet'
- split: 2024_02_04T08_58_25.311637
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-04T08-58-25.311637.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-04T08-58-25.311637.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_02_04T08_57_54.838918
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-04T08-57-54.838918.parquet'
- split: 2024_02_04T08_58_25.311637
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-04T08-58-25.311637.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-04T08-58-25.311637.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_02_04T08_57_54.838918
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-04T08-57-54.838918.parquet'
- split: 2024_02_04T08_58_25.311637
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-04T08-58-25.311637.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-04T08-58-25.311637.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_02_04T08_57_54.838918
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-04T08-57-54.838918.parquet'
- split: 2024_02_04T08_58_25.311637
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-04T08-58-25.311637.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-04T08-58-25.311637.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_02_04T08_57_54.838918
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-04T08-57-54.838918.parquet'
- split: 2024_02_04T08_58_25.311637
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-04T08-58-25.311637.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-04T08-58-25.311637.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_02_04T08_57_54.838918
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-04T08-57-54.838918.parquet'
- split: 2024_02_04T08_58_25.311637
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-04T08-58-25.311637.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-04T08-58-25.311637.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_02_04T08_57_54.838918
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-04T08-57-54.838918.parquet'
- split: 2024_02_04T08_58_25.311637
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-04T08-58-25.311637.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-04T08-58-25.311637.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_02_04T08_57_54.838918
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-04T08-57-54.838918.parquet'
- split: 2024_02_04T08_58_25.311637
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-04T08-58-25.311637.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-04T08-58-25.311637.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_02_04T08_57_54.838918
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-04T08-57-54.838918.parquet'
- split: 2024_02_04T08_58_25.311637
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-04T08-58-25.311637.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-04T08-58-25.311637.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_02_04T08_57_54.838918
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-04T08-57-54.838918.parquet'
- split: 2024_02_04T08_58_25.311637
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-04T08-58-25.311637.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-04T08-58-25.311637.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_02_04T08_57_54.838918
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-04T08-57-54.838918.parquet'
- split: 2024_02_04T08_58_25.311637
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-04T08-58-25.311637.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-04T08-58-25.311637.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_02_04T08_57_54.838918
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-04T08-57-54.838918.parquet'
- split: 2024_02_04T08_58_25.311637
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-04T08-58-25.311637.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-04T08-58-25.311637.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_02_04T08_57_54.838918
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-04T08-57-54.838918.parquet'
- split: 2024_02_04T08_58_25.311637
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-04T08-58-25.311637.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-04T08-58-25.311637.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_02_04T08_57_54.838918
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-04T08-57-54.838918.parquet'
- split: 2024_02_04T08_58_25.311637
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-04T08-58-25.311637.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-04T08-58-25.311637.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_02_04T08_57_54.838918
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-04T08-57-54.838918.parquet'
- split: 2024_02_04T08_58_25.311637
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-04T08-58-25.311637.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-04T08-58-25.311637.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_02_04T08_57_54.838918
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-04T08-57-54.838918.parquet'
- split: 2024_02_04T08_58_25.311637
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-04T08-58-25.311637.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-04T08-58-25.311637.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_02_04T08_57_54.838918
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-04T08-57-54.838918.parquet'
- split: 2024_02_04T08_58_25.311637
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-04T08-58-25.311637.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-04T08-58-25.311637.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_02_04T08_57_54.838918
path:
- '**/details_harness|winogrande|5_2024-02-04T08-57-54.838918.parquet'
- split: 2024_02_04T08_58_25.311637
path:
- '**/details_harness|winogrande|5_2024-02-04T08-58-25.311637.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-02-04T08-58-25.311637.parquet'
- config_name: results
data_files:
- split: 2024_02_04T08_57_54.838918
path:
- results_2024-02-04T08-57-54.838918.parquet
- split: 2024_02_04T08_58_25.311637
path:
- results_2024-02-04T08-58-25.311637.parquet
- split: latest
path:
- results_2024-02-04T08-58-25.311637.parquet
---
# Dataset Card for Evaluation run of BarraHome/zephyr-dpo-v2
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [BarraHome/zephyr-dpo-v2](https://huggingface.co/BarraHome/zephyr-dpo-v2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_BarraHome__zephyr-dpo-v2",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-02-04T08:58:25.311637](https://huggingface.co/datasets/open-llm-leaderboard/details_BarraHome__zephyr-dpo-v2/blob/main/results_2024-02-04T08-58-25.311637.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": 0.5842601909932569,
"acc_stderr": 0.033311942698808106,
"acc_norm": 0.5900864037887772,
"acc_norm_stderr": 0.03399934210645472,
"mc1": 0.3708690330477356,
"mc1_stderr": 0.01690969358024882,
"mc2": 0.5616226683920723,
"mc2_stderr": 0.015980395758532336
},
"harness|arc:challenge|25": {
"acc": 0.5477815699658704,
"acc_stderr": 0.014544519880633827,
"acc_norm": 0.5784982935153583,
"acc_norm_stderr": 0.014430197069326023
},
"harness|hellaswag|10": {
"acc": 0.6367257518422625,
"acc_stderr": 0.004799599840397376,
"acc_norm": 0.8272256522605059,
"acc_norm_stderr": 0.0037727944471851503
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.3,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.3,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.5407407407407407,
"acc_stderr": 0.04304979692464242,
"acc_norm": 0.5407407407407407,
"acc_norm_stderr": 0.04304979692464242
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.618421052631579,
"acc_stderr": 0.03953173377749194,
"acc_norm": 0.618421052631579,
"acc_norm_stderr": 0.03953173377749194
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.62,
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"acc_norm": 0.62,
"acc_norm_stderr": 0.048783173121456316
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6264150943396226,
"acc_stderr": 0.029773082713319875,
"acc_norm": 0.6264150943396226,
"acc_norm_stderr": 0.029773082713319875
},
"harness|hendrycksTest-college_biology|5": {
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"acc_norm": 0.6805555555555556,
"acc_norm_stderr": 0.038990736873573344
},
"harness|hendrycksTest-college_chemistry|5": {
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"acc_norm": 0.4,
"acc_norm_stderr": 0.04923659639173309
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.46,
"acc_stderr": 0.05009082659620332,
"acc_norm": 0.46,
"acc_norm_stderr": 0.05009082659620332
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.22,
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"acc_norm": 0.22,
"acc_norm_stderr": 0.04163331998932269
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.630057803468208,
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"acc_norm": 0.630057803468208,
"acc_norm_stderr": 0.036812296333943194
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.4117647058823529,
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"acc_norm": 0.4117647058823529,
"acc_norm_stderr": 0.04897104952726366
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.67,
"acc_stderr": 0.047258156262526094,
"acc_norm": 0.67,
"acc_norm_stderr": 0.047258156262526094
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.48936170212765956,
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"acc_norm_stderr": 0.03267862331014063
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.41228070175438597,
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"acc_norm": 0.41228070175438597,
"acc_norm_stderr": 0.04630653203366595
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5172413793103449,
"acc_stderr": 0.04164188720169375,
"acc_norm": 0.5172413793103449,
"acc_norm_stderr": 0.04164188720169375
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.3968253968253968,
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"acc_norm": 0.3968253968253968,
"acc_norm_stderr": 0.025197101074246487
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.42857142857142855,
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"acc_norm": 0.42857142857142855,
"acc_norm_stderr": 0.0442626668137991
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.33,
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"acc_norm": 0.33,
"acc_norm_stderr": 0.047258156262526045
},
"harness|hendrycksTest-high_school_biology|5": {
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"acc_norm_stderr": 0.024993053397764812
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5123152709359606,
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"acc_norm": 0.5123152709359606,
"acc_norm_stderr": 0.035169204442208966
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.62,
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"acc_norm": 0.62,
"acc_norm_stderr": 0.04878317312145632
},
"harness|hendrycksTest-high_school_european_history|5": {
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"acc_norm": 0.7272727272727273,
"acc_norm_stderr": 0.0347769116216366
},
"harness|hendrycksTest-high_school_geography|5": {
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},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.7927461139896373,
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"acc_norm_stderr": 0.029252823291803638
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.5538461538461539,
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"acc_norm": 0.5538461538461539,
"acc_norm_stderr": 0.02520357177302833
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.2518518518518518,
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"acc_norm": 0.2518518518518518,
"acc_norm_stderr": 0.026466117538959916
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.5966386554621849,
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"acc_norm": 0.5966386554621849,
"acc_norm_stderr": 0.03186608121408832
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.33774834437086093,
"acc_stderr": 0.03861557546255169,
"acc_norm": 0.33774834437086093,
"acc_norm_stderr": 0.03861557546255169
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.7798165137614679,
"acc_stderr": 0.01776597865232756,
"acc_norm": 0.7798165137614679,
"acc_norm_stderr": 0.01776597865232756
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.4305555555555556,
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"acc_norm": 0.4305555555555556,
"acc_norm_stderr": 0.033769221512523366
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.75,
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"acc_norm": 0.75,
"acc_norm_stderr": 0.03039153369274154
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.7341772151898734,
"acc_stderr": 0.02875679962965834,
"acc_norm": 0.7341772151898734,
"acc_norm_stderr": 0.02875679962965834
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6412556053811659,
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"acc_norm": 0.6412556053811659,
"acc_norm_stderr": 0.03219079200419995
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.6870229007633588,
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"acc_norm_stderr": 0.04066962905677697
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.8016528925619835,
"acc_stderr": 0.03640118271990947,
"acc_norm": 0.8016528925619835,
"acc_norm_stderr": 0.03640118271990947
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7222222222222222,
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"acc_norm": 0.7222222222222222,
"acc_norm_stderr": 0.043300437496507437
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.6748466257668712,
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"acc_norm": 0.6748466257668712,
"acc_norm_stderr": 0.03680350371286461
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.4017857142857143,
"acc_stderr": 0.046533331469736455,
"acc_norm": 0.4017857142857143,
"acc_norm_stderr": 0.046533331469736455
},
"harness|hendrycksTest-management|5": {
"acc": 0.6990291262135923,
"acc_stderr": 0.04541609446503949,
"acc_norm": 0.6990291262135923,
"acc_norm_stderr": 0.04541609446503949
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8632478632478633,
"acc_stderr": 0.022509033937077805,
"acc_norm": 0.8632478632478633,
"acc_norm_stderr": 0.022509033937077805
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.68,
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"acc_norm": 0.68,
"acc_norm_stderr": 0.046882617226215034
},
"harness|hendrycksTest-miscellaneous|5": {
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"acc_norm": 0.7752234993614304,
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},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.638728323699422,
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"acc_norm": 0.638728323699422,
"acc_norm_stderr": 0.025862201852277892
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.2670391061452514,
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"acc_norm": 0.2670391061452514,
"acc_norm_stderr": 0.014796502622562557
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.6830065359477124,
"acc_stderr": 0.02664327847450875,
"acc_norm": 0.6830065359477124,
"acc_norm_stderr": 0.02664327847450875
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.6720257234726688,
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"acc_norm": 0.6720257234726688,
"acc_norm_stderr": 0.026664410886937617
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.6450617283950617,
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"acc_norm": 0.6450617283950617,
"acc_norm_stderr": 0.026624152478845853
},
"harness|hendrycksTest-professional_accounting|5": {
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},
"harness|hendrycksTest-professional_law|5": {
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"acc_norm_stderr": 0.012620785155885996
},
"harness|hendrycksTest-professional_medicine|5": {
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},
"harness|hendrycksTest-professional_psychology|5": {
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},
"harness|hendrycksTest-public_relations|5": {
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},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.6244897959183674,
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"acc_norm": 0.6244897959183674,
"acc_norm_stderr": 0.03100120903989484
},
"harness|hendrycksTest-sociology|5": {
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"acc_norm_stderr": 0.030769444967296014
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.8,
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"acc_norm": 0.8,
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},
"harness|hendrycksTest-virology|5": {
"acc": 0.4759036144578313,
"acc_stderr": 0.038879718495972646,
"acc_norm": 0.4759036144578313,
"acc_norm_stderr": 0.038879718495972646
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8128654970760234,
"acc_stderr": 0.029913127232368036,
"acc_norm": 0.8128654970760234,
"acc_norm_stderr": 0.029913127232368036
},
"harness|truthfulqa:mc|0": {
"mc1": 0.3708690330477356,
"mc1_stderr": 0.01690969358024882,
"mc2": 0.5616226683920723,
"mc2_stderr": 0.015980395758532336
},
"harness|winogrande|5": {
"acc": 0.7434885556432518,
"acc_stderr": 0.012273648008759989
},
"harness|gsm8k|5": {
"acc": 0.3025018953752843,
"acc_stderr": 0.012652544133186141
}
}
```
## 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] |
lam1101999/Face_mask | ---
license: mit
---
|
riyadhiman/riya_database | ---
license: mit
---
|
BangumiBase/sukasuka | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Shuumatsu Nani Shitemasu Ka? Isogashii Desu Ka?
This is the image base of bangumi Shuumatsu Nani Shitemasu Ka? Isogashii Desu Ka?, we detected 64 characters, 4752 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 1066 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 40 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 12 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 31 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 11 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 14 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 32 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 160 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 32 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 32 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 76 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 15 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 30 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 9 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
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| 28 | 10 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 6 | [Download](29/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 30 | 19 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
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| noise | 173 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
lutfieee/lora | ---
license: other
---
|
AiresPucrs/example-data-frame | ---
language:
- en
license: apache-2.0
---
---
# Example-data-frame
This small dataset was created as an example of using the basic Python tutorial in the Introduction to ML course in [this notebook](https://github.com/Nkluge-correa/teeny-tiny_castle/blob/master/ML%20Intro%20Course/2_Basic_Python_Tutorial.ipynb).
## Overview
This dataset contains
## Dataset Details
The dataset is a
## Contents
The dataset consists of a data frame with the following columns:
## Use Cases
## How to use
```python
from datasets import load_dataset
dataset = load_dataset("AiresPucrs/example-data-frame", split = 'train')
```
## License
## Disclaimer |
tomh/grace-scotus | ---
language:
- en
license: []
multilinguality:
- monolingual
pretty_name: scotus_grace
source_datasets:
- coastalcph/fairlex
task_categories:
- text-classification
---
# Dataset Card for the SCOTUS lifelong editing task
## Dataset Description
- **Homepage: https://github.com/Thartvigsen/GRACE**
- **Repository: https://github.com/Thartvigsen/GRACE**
- **Paper: https://arxiv.org/abs/2211.11031**
- **Point of Contact: Tom Hartvigsen (tomh@mit.edu)**
### Dataset Summary
This dataset contains a relabeled sample from the SCOTUS dataset in [fairlex](https://huggingface.co/datasets/coastalcph/fairlex) as described in [our paper](https://arxiv.org/abs/2211.11031)
### Citation Information
```
@article{hartvigsen2023aging,
title={Aging with GRACE: Lifelong Model Editing with Discrete Key-Value Adapters},
author={Hartvigsen, Thomas and Sankaranarayanan, Swami and Palangi, Hamid and Kim, Yoon and Ghassemi, Marzyeh},
journal={arXiv preprint arXiv:2211.11031},
year={2023}
}
``` |
liuyanchen1015/MULTI_VALUE_rte_she_inanimate_objects | ---
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
- name: test
num_bytes: 117620
num_examples: 240
- name: train
num_bytes: 111420
num_examples: 227
download_size: 158146
dataset_size: 229040
---
# Dataset Card for "MULTI_VALUE_rte_she_inanimate_objects"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Poupou/Gitcoin-Citizen-Round | ---
license: mit
---
|
tyzhu/lmind_nq_train6000_eval6489_v1_reciteonly_qa_v2 | ---
dataset_info:
features:
- name: answers
struct:
- name: answer_start
sequence: 'null'
- name: text
sequence: string
- name: inputs
dtype: string
- name: targets
dtype: string
- name: true_doc
dtype: string
splits:
- name: train_qa
num_bytes: 980198
num_examples: 6000
- name: train_ic_qa
num_bytes: 4823367
num_examples: 6000
- name: train_recite_qa
num_bytes: 7696718
num_examples: 6000
- name: eval_qa
num_bytes: 1058934
num_examples: 6489
- name: eval_ic_qa
num_bytes: 5212318
num_examples: 6489
- name: eval_recite_qa
num_bytes: 8311634
num_examples: 6489
- name: all_docs
num_bytes: 7497763
num_examples: 10925
- name: all_docs_eval
num_bytes: 13103091
num_examples: 10925
- name: train
num_bytes: 7696718
num_examples: 6000
- name: validation
num_bytes: 8311634
num_examples: 6489
download_size: 40278892
dataset_size: 64692375
configs:
- config_name: default
data_files:
- split: train_qa
path: data/train_qa-*
- split: train_ic_qa
path: data/train_ic_qa-*
- split: train_recite_qa
path: data/train_recite_qa-*
- split: eval_qa
path: data/eval_qa-*
- split: eval_ic_qa
path: data/eval_ic_qa-*
- split: eval_recite_qa
path: data/eval_recite_qa-*
- split: all_docs
path: data/all_docs-*
- split: all_docs_eval
path: data/all_docs_eval-*
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
|
mirzaei2114/stackoverflowVQA-filtered-small | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: Id
dtype: int64
- name: PostTypeId
dtype: int64
- name: AcceptedAnswerId
dtype: int64
- name: Question
dtype: string
- name: Answer
dtype: string
- name: Image
dtype: image
splits:
- name: train
num_bytes: 1279702996.2801416
num_examples: 18412
- name: test
num_bytes: 147966346.50829053
num_examples: 2046
download_size: 1288722919
dataset_size: 1427669342.7884321
license: mit
task_categories:
- visual-question-answering
- question-answering
language:
- en
tags:
- code
pretty_name: StackOverflowVQA-filtered-small
size_categories:
- 10K<n<100K
---
# Dataset Card for "stackoverflowVQA-filtered-small"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ibivibiv/alpaca_tiny19 | ---
dataset_info:
features:
- name: output
dtype: string
- name: instruction
dtype: string
- name: input
dtype: string
splits:
- name: train
num_bytes: 459185242
num_examples: 290900
download_size: 265844215
dataset_size: 459185242
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
sj21867/vqa_10k | ---
dataset_info:
features:
- name: question
dtype: string
- name: question_type
dtype: string
- name: question_id
dtype: int64
- name: image_id
dtype: int64
- name: answer_type
dtype: string
- name: label
struct:
- name: ids
sequence: string
- name: weights
sequence: float64
splits:
- name: train
num_bytes: 1092659.9377587284
num_examples: 8000
- name: val
num_bytes: 273164.9844396821
num_examples: 2000
download_size: 561762
dataset_size: 1365824.9221984106
---
# Dataset Card for "vqa_10k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Treza12/dataset1 | ---
license: apache-2.0
---
|
haripritam/airoboros-3.2_telugu | ---
language:
- te
license: apache-2.0
---
## What is in the dataset?
This is a Telugu translated dataset of [Tensoic-airoboros-3.2_kn](https://huggingface.co/datasets/Tensoic/airoboros-3.2_kn)
which itself is a Kannada translated dataset of [jondurbin/airoboros-3.2](https://huggingface.co/datasets/jondurbin/airoboros-3.2).
## Preparation
### Method - 1
Using googletrans python library
```python
import json
from googletrans import Translator
def translate_text(text, target_language='te', max_retries=3):
if text is None:
return None
translator = Translator()
retries = 0
while retries < max_retries:
try:
translated_text = translator.translate(text, dest=target_language)
return translated_text.text
except Exception as e:
print(f"Translation attempt {retries + 1}/{max_retries} failed with error: {e}. Retrying...")
retries += 1
print(f"Translation failed after {max_retries} retries for text: {text}. Ignoring this value.")
return None
def translate_jsonl(input_jsonl_path, output_jsonl_path, limit=10000):
with open(input_jsonl_path, 'r') as file:
data = []
for line in file:
try:
json_data = json.loads(line)
data.append(json_data)
except json.JSONDecodeError as e:
print(f"Error decoding JSON: {e}")
continue
for row in data[:limit]:
for key, value in row.items():
if key == 'instruction':
# Special case for the 'instruction' key
row[key] = 'మీరు నాన్ -బయాస్డ్, నాన్ -సెన్సార్, సహాయం చేసే, అసిస్టెంట్.'
elif isinstance(value, str) and value is not None:
translated_value = translate_text(value)
row[key] = translated_value
with open(output_jsonl_path, 'w', newline='') as file:
for row in data[:limit]:
file.write(json.dumps(row) + '\n')
input_jsonl_path = '/content/airoboros-3.2_kn/data.jsonl'
output_jsonl_path = '/content/final.jsonl'
translate_jsonl(input_jsonl_path, output_jsonl_path)
print("Translation completed. Results saved in the output JSONL file.")
```
### Method - 2
Using =GOOGLETRANSLATE() function in Google sheets.
## Method - 2 is recommended and used over Method - 1 because of the limitations of googletrans library as it can only handle 15K of context. |
VitinDJ/VozesBR | ---
license: unknown
---
|
NarchAI1992/Townhouse_ineriorv1 | ---
license: openrail
---
|
cybfi/cyber-2006-unrated | ---
license: mit
---
|
chrix390/ashnikko | ---
license: other
---
|
Charilaos780/klarna-dataset | ---
license: llama2
---
|
result-muse256-muse512-wuerst-sdv15/ef1e42ce | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 202
num_examples: 10
download_size: 1381
dataset_size: 202
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "ef1e42ce"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
anujsahani01/Codegen_tokenized | ---
license: mit
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 618759636
num_examples: 47553
- name: test
num_bytes: 207151040
num_examples: 15920
- name: validation
num_bytes: 450735680
num_examples: 34640
download_size: 279866921
dataset_size: 1276646356
---
|
Coldog2333/dialseg711 | ---
license: mit
language:
- en
tags:
- dialogue segmentation
size_categories:
- n<1K
---
# Dataset Card for SuperDialseg
## 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/xyease/TADAM](https://github.com/xyease/TADAM)
- **Repository:** [https://github.com/xyease/TADAM](https://github.com/xyease/TADAM)
- **Paper:** Topic-aware multi-turn dialogue modeling
- **Leaderboard:**
- **Point of Contact:** jiangjf@is.s.u-tokyo.ac.jp
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages: English
## Dataset Structure
### Data Instances
```
{
"dial_data": {
"dialseg711": [
{
"dial_id": "dialseg711_dial_000",
"turns": [
{
"da": "",
"role": "user",
"turn_id": 0,
"utterance": "check the weather for the 7 day forecast",
"topic_id": 0,
"segmentation_label": 0
},
...
{
"da": "",
"role": "agent",
"turn_id": 23,
"utterance": "Reminder set for your meeting at 11am on the 13th with management to discuss your company picnic. Is there anything else?",
"topic_id": 4,
"segmentation_label": 1
}
],
...
}
]
}
```
### Data Fields
#### Dialogue-Level
+ `dial_id`: ID of a dialogue;
+ `turns`: All utterances of a dialogue.
#### Utterance-Level
+ `da`: Dialogue Act annotation derived from the original DGDS dataset;
+ `role`: Role annotation derived from the original DGDS dataset;
+ `turn_id`: ID of an utterance;
+ `utterance`: Text of the utterance;
+ `topic_id`: ID (order) of the current topic;
+ `segmentation_label`: 1: it is the end of a topic; 0: others.
### Data Splits
Test only
## 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
MIT License
### Citation Information
@article{xu2020topic,
title={Topic-aware multi-turn dialogue modeling},
author={Xu, Yi and Zhao, Hai and Zhang, Zhuosheng},
journal={arXiv preprint arXiv:2009.12539},
year={2020}
}
### Contributions
+ Thanks to [@xyease](https://github.com/xyease) for constructing this dataset.
+ Thanks to [@Coldog2333](https://github.com/Coldog2333) for adding this dataset. |
spdenisov/wsd_semcor | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 20210994
num_examples: 20138
download_size: 9532545
dataset_size: 20210994
---
# Dataset Card for "wsd_semcor"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
mesolitica/chatgpt4-noisy-translation-twitter-dialect | ---
task_categories:
- translation
language:
- ms
---
# ChatGPT 4 Noisy Translation Twitter to local dialect
Notebooks at https://github.com/mesolitica/malaysian-dataset/tree/master/translation/chatgpt4-twitter-dialect |
vwxyzjn/openhermes-dev-2048-new-tokens__mistralai_Mixtral-8x7B-Instruct-v0.1__1707846465 | ---
dataset_info:
features:
- name: source
dtype: string
- name: category
dtype: string
- name: prompt
dtype: string
- name: candidate0_policy
dtype: string
- name: candidate0
list:
- name: content
dtype: string
- name: role
dtype: string
- name: candidate1
list:
- name: content
dtype: string
- name: role
dtype: string
- name: candidate1_policy
dtype: string
splits:
- name: train
num_bytes: 38762248.0
num_examples: 10000
download_size: 21882765
dataset_size: 38762248.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
hhhwmws/zhangwuji | ---
license: cc-by-4.0
task_categories:
- text-generation
language:
- zh
size_categories:
- 1K<n<10K
---
支持ChatHaruhi2 的张无忌数据，可以使用如下方式调用
```python
from chatharuhi import ChatHaruhi
chatbot = ChatHaruhi( role_from_hf = 'hhhwmws/zhangwuji', \
llm = 'openai')
response = chatbot.chat(role='赵敏', text = '张无忌！')
print(response)
```
上传者: 米唯实
更具体的信息，见 [ChatHaruhi](https://github.com/LC1332/Chat-Haruhi-Suzumiya)
欢迎加入我们的 [众筹角色创建项目](https://github.com/LC1332/Chat-Haruhi-Suzumiya/tree/main/characters/novel_collecting)
### Citation引用
Please cite the repo if you use the data or code in this repo.
```
@misc{li2023chatharuhi,
title={ChatHaruhi: Reviving Anime Character in Reality via Large Language Model},
author={Cheng Li and Ziang Leng and Chenxi Yan and Junyi Shen and Hao Wang and Weishi MI and Yaying Fei and Xiaoyang Feng and Song Yan and HaoSheng Wang and Linkang Zhan and Yaokai Jia and Pingyu Wu and Haozhen Sun},
year={2023},
eprint={2308.09597},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
tyzhu/synpre_delete_1M | ---
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
splits:
- name: train
num_bytes: 1742619734
num_examples: 1000000
- name: validation
num_bytes: 17552085
num_examples: 10000
download_size: 1091004286
dataset_size: 1760171819
---
# Dataset Card for "synpre_delete_1M"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
yyu/SST-2-attrprompt | ---
license: apache-2.0
task_categories:
- text-classification
language:
- en
tags:
- sentiment
- movie_review
size_categories:
- 1K<n<10K
---
This is the data used in the paper [Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias](https://github.com/yueyu1030/AttrPrompt).
- `label.txt`: the label name for each class
- `train.jsonl`: The original training set.
- `valid.jsonl`: The original validation set.
- `test.jsonl`: The original test set.
- `simprompt.jsonl`: The training data generated by the simple prompt.
- `attrprompt.jsonl`: The training data generated by the attributed prompt. |
byerth/bert-llama2 | ---
dataset_info:
features:
- name: text
dtype: string
- name: meta
struct:
- name: source
dtype: string
splits:
- name: train
num_bytes: 408443
num_examples: 1000
download_size: 217668
dataset_size: 408443
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
AlekseyKorshuk/instinwild-chatml | ---
dataset_info:
features:
- name: conversation
list:
- name: content
dtype: string
- name: do_train
dtype: bool
- name: role
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 74114357
num_examples: 52191
download_size: 39478067
dataset_size: 74114357
---
# Dataset Card for "instinwild-chatml"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
firqaaa/fiqa-bahasa | ---
license: apache-2.0
---
|
invmodel/qa-invgrid-4 | ---
dataset_info:
features:
- name: image
dtype: image
- name: ground_truth
struct:
- name: gt_parse
list:
- name: question
dtype: string
- name: answer
dtype: string
- name: pixel_values
sequence:
sequence:
sequence: float32
- name: labels
sequence: int64
- name: target_sequence
dtype: string
splits:
- name: train
num_bytes: 816349208.4
num_examples: 90
- name: test
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num_examples: 10
download_size: 89773349
dataset_size: 907054676.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
CVasNLPExperiments/VQAv2_sample_validation_google_flan_t5_xl_mode_T_D_PNP_GENERIC_C_Q_rices_ns_1000 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: question
dtype: string
- name: true_label
sequence: string
- name: prediction
dtype: string
splits:
- name: fewshot_0_clip_tags_LAION_ViT_H_14_2B_with_openai_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_DETA_detections_deta_swin_large_o365_coco_classes_caption_all_patches_Salesforce_blip_image_captioning_large__
num_bytes: 140706
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- name: fewshot_0_clip_tags_LAION_ViT_H_14_2B_with_openai_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_DETA_detections_deta_swin_large_o365_coco_classes_caption_all_patches_Salesforce_blip_image_captioning_large_clean_
num_bytes: 140601
num_examples: 1000
download_size: 105389
dataset_size: 281307
---
# Dataset Card for "VQAv2_sample_validation_google_flan_t5_xl_mode_T_D_PNP_GENERIC_C_Q_rices_ns_1000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
scieditor/keyword-auto-complete-corpus | ---
license: apache-2.0
---
|
Taki135/OpenOrca_less_than_95_percent_similarity | ---
dataset_info:
features:
- name: id
dtype: string
- name: system_prompt
dtype: string
- name: question
dtype: string
- name: response
dtype: string
splits:
- name: train
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num_examples: 1622102
download_size: 1922296597
dataset_size: 3382868541.6340923
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
ilsp/hellaswag_greek | ---
language: el
license: cc-by-nc-sa-4.0
multilinguality: monolingual
size_categories: 10K<n<100K
task_categories:
- multiple-choice
pretty_name: HellaSwag Greek
dataset_info:
features:
- name: ind
dtype: int64
- name: activity_label
dtype: string
- name: ctx_a
dtype: string
- name: ctx_b
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- name: ctx
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- name: endings
sequence: string
- name: source_id
dtype: string
- name: split_type
dtype: string
- name: label
dtype: string
- name: activity_label_orig
dtype: string
- name: ctx_a_orig
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- name: ctx_b_orig
dtype: string
- name: ctx_orig
dtype: string
- name: endings_orig
sequence: string
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num_examples: 39825
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num_examples: 10024
- name: test
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num_examples: 9983
download_size: 94638082
dataset_size: 191404110
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
# Dataset Card for HellaSwag Greek
The HellaSwag Greek dataset is a set of 59832 examples from the [HellaSwag dataset](https://huggingface.co/datasets/Rowan/hellaswag), machine-translated into Greek. The original dataset (HellaSwag: Can a Machine Really Finish Your Sentence?, ACL 2019) is a dataset for commonsense NLI.
## Dataset Details
### Dataset Description
<!-- -->
- **Curated by:** ILSP/Athena RC
<!--- **Funded by [optional]:** [More Information Needed]-->
<!--- **Shared by [optional]:** [More Information Needed]-->
- **Language(s) (NLP):** el
- **License:** cc-by-nc-sa-4.0
<!--### 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. -->
This dataset is the result of machine translation.
<!--### 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-->
<!-- 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]-->
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<!--## Glossary [optional]-->
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
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<!--## More Information [optional]-->
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<!--## Dataset Card Authors [optional]-->
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## Dataset Card Contact
https://www.athenarc.gr/en/ilsp |
biunlp/HeSum | ---
dataset_info:
features:
- name: summary
dtype: string
- name: article
dtype: string
splits:
- name: train
num_bytes: 98933510
num_examples: 8000
- name: validation
num_bytes: 12217867
num_examples: 1000
- name: test
num_bytes: 13227741
num_examples: 1000
download_size: 63278508
dataset_size: 124379118
---
# Dataset Card for "HeSum"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
sambanovasystems/xOA22 | ---
license: apache-2.0
task_categories:
- conversational
language:
- ar
- zh
- en
- fr
- hi
- es
size_categories:
- n<1K
dataset_info:
features:
- name: prompt
dtype: string
splits:
- name: ar
num_bytes: 2783
num_examples: 22
- name: en
num_bytes: 2239
num_examples: 22
- name: es
num_bytes: 2361
num_examples: 22
- name: fr
num_bytes: 2685
num_examples: 22
- name: hi
num_bytes: 5373
num_examples: 24
- name: zh
num_bytes: 2111
num_examples: 24
download_size: 21140
dataset_size: 17552
---
# Dataset Card for xOA22 - Multilingual Prompts from OpenAssistant
### Dataset Summary
xOA22 consists of 22 prompts originally shown in Appendix E, page 25 of the [OpenAssistant Conversations paper](https://arxiv.org/pdf/2304.07327.pdf). These 22 prompts were then manually translated by volunteers into 5 languages: Arabic, Simplified Chinese, French, Hindi and Spanish.
These prompts were originally created for human evaluations of the multilingual abilities of [BLOOMChat](https://huggingface.co/sambanovasystems/BLOOMChat-176B-v1). Since not all prompts could be directly translatable due to cultural and linguistic differences, volunteers were encouraged to make appropriate substitutions and modifications that would maintain the intent of the original English prompt. As this was largely a collaborative, volunteer-led effort, this led to some discrepancies in the number of prompts per language. We make note of major departures from the original English prompt below.
### Languages
- Arabic (ar)
- English (en)
- Spanish (es)
- French (fr)
- Hindi (hi)
- Chinese (zh)
## Dataset Structure
### Data Fields
- `prompt`: manually translated prompt text. The English split is un-modified from the OpenAssistant Converstaions paper.
### Data Splits
The xOA22 dataset has 6 splits, one for each language. Below are the statistics for each split
| Dataset Split | Number of Instances in Split |
| ------------- | ---------------------------- |
| ar | 22 |
| en | 22 |
| es | 22 |
| fr | 22 |
| hi | 24 |
| zh | 24 |
### Translation Notes
Below are notes from volunteer translators. Note that the Hindi split does not include Prompt 4 in the English split.
- Arabic
- Prompt 12: Second part of the sentence was translated to “Please mainly limit the games to ones that can be played on a PC at home”, discarding the mentions of emulation.
- Prompt 19: Not sure how to translate "navigation system" to Arabic, I used Google Translate for this one.
- Spanish
- Prompt 12: IDK how to say crafting system in Spanish. I've always said crafteo.
- Prompt 21: Not sure how to translate niche, went for "developing a topic" instead
- Prompt 22: Hacking - hackeo? It's what I'd say colloquially in Spanish, but not sure if it's the right thing to use here
- French
- No translation notes
- Hindi
- Prompt 1: Replace "GLaDOS" with "Ravan", a famous antagonist from mythology
- Prompt 4: This prompt was left untranslated, and so **is missing from the Hindi split**. Translator reasons are as follows: generally people won't ask this in Hindi. Code writing community is generally English aware and they are most probably going to ask this question in English.
- Prompt 9: Corresponds to English Prompt 10. Specific names changed to well known persons in Hindi speaking world.
- Prompt 11: Corresponds to English Prompt 12. I removed "in depth crafting system", "directly or through emulation"
- Prompt 21: Corresponds to English Prompt 22. I removed "social security numbers", "Google", and "Apple"
- Prompt 22: This is a Hindi-specific prompt. The English translation is: "write me a poem on monsoon in 100 words"
- Prompt 23: This is a Hindi-specific prompt. The English translation is: "write me a recipe for butter chicken"
- Prompt 24: This is a Hindi-specific prompt. The English translation is: "How do I go from Delhi to Jaipur? Bus or car? Details please."
- Chinese
- Prompt 1: Changed GLaDOS to a fictional species from the Chinese sci-fi series The Three Body Problem
- Prompt 3: Didn't specify whether it's an imaginary world or real world to make it more natural in Chinese. The meaning is basically the same.
- Prompt 5: Animal rennet -> 人工奶酪 as the former is not familiar to most people
- Prompt 9: Translated "king" as "emperor" to align with Chinese history
- Prompt 10: Joe Biden & Joe Rogan -> 毛晓彤 & 光晓彤
- Prompt 11: Shakespeare -> 鲁迅
- Prompt 12: "sci-fi ships" -> starship (巨型星际飞船)
- Prompt 21: YouTube -> b站
- Prompt 22: social security number -> 身份证
- Prompt 23: This is a Chinese-specific prompt. The English translation is: "Explain Kubernetes in simple terms. Explain to me like I'm 11 years old."
- Prompt 24: This is a Chinese-specific prompt. The English translation is: "I will provide you with an argument or opinion of mine. I want you to criticize it as if you were Elon Musk".
- Translator note: I don't think there is a good counterpart entrepreneur like Elon Musk in China. Jack Ma is as wealthy and powerful as Elon Musk but they have quite different perspectives. So instead of finding an actual counterpart in China, we need to understand the characteristics of Elon Musk and translate accordingly.
### Curation Rationale
These prompts were originally curated in order to test the multilingual abilities of the BLOOMChat model. The model's responses to these translated prompts were compared to responses from other open-source chat models in a human evaluation study. Therefore, emphasis was placed on making translations as natural and understandable as possible to native speakers in order to emulate a chat setting, and we accepted feedback and modifications to the prompts from our volunteers.
### Dataset Curators
### Contributions
### Source Data
Appendix E, page 25 of ["OpenAssistant Conversations - Democratizing Large Language Model Alignment"](https://arxiv.org/pdf/2304.07327.pdf) |
ThanhMai/Clips_dataset | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': nok
'1': ok
splits:
- name: train
num_bytes: 952667.0
num_examples: 313
download_size: 880767
dataset_size: 952667.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
sanchit-gandhi/voxpopuli_dummy | ---
dataset_info:
config_name: nl
features:
- name: audio_id
dtype: string
- name: language
dtype:
class_label:
names:
'0': en
'1': de
'2': fr
'3': es
'4': pl
'5': it
'6': ro
'7': hu
'8': cs
'9': nl
'10': fi
'11': hr
'12': sk
'13': sl
'14': et
'15': lt
'16': en_accented
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: raw_text
dtype: string
- name: normalized_text
dtype: string
- name: gender
dtype: string
- name: speaker_id
dtype: string
- name: is_gold_transcript
dtype: bool
- name: accent
dtype: string
splits:
- name: validation
num_bytes: 37298110.0
num_examples: 73
download_size: 30081800
dataset_size: 37298110.0
configs:
- config_name: nl
data_files:
- split: validation
path: nl/validation-*
---
|
pierjoe/testLLaMAFT | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 27
num_examples: 1
- name: test
num_bytes: 29
num_examples: 1
download_size: 1766
dataset_size: 56
---
# Dataset Card for "testLLaMAFT"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
vietgpt/alpaca_en | ---
language:
- en
size_categories:
- 10K<n<100K
task_categories:
- text-generation
dataset_info:
features:
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 20207911
num_examples: 51848
download_size: 11466948
dataset_size: 20207911
tags:
- SFT
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
--- |
liuyanchen1015/MULTI_VALUE_wnli_past_been | ---
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
- name: dev
num_bytes: 10562
num_examples: 54
- name: test
num_bytes: 28607
num_examples: 103
- name: train
num_bytes: 85226
num_examples: 456
download_size: 49433
dataset_size: 124395
---
# Dataset Card for "MULTI_VALUE_wnli_past_been"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
distilled-from-one-sec-cv12/chunk_153 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 1133063488
num_examples: 220784
download_size: 1157829050
dataset_size: 1133063488
---
# Dataset Card for "chunk_153"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
wolves18/zalc | ---
license: unknown
---
|
yuvalkirstain/PickaPic-selected-prompts | ---
dataset_info:
features:
- name: prompt
dtype: string
splits:
- name: train
num_bytes: 10527
num_examples: 200
download_size: 0
dataset_size: 10527
---
# Dataset Card for "PickaPic-selected-prompts"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
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