keti
Datasets
All datasets matching “keti”KoEVD
KoEVD
KoEVD is a Korean benchmark linking five evaluation or analysis targets through source utterances: utterance-risk judgment, candidate-response safety choice, direct-generation response harmfulness, descriptive response strategies, and a pre-execution mock tool/action-choice diagnostic.
Contents and scope
The canonical corpus contains 13,552 sources and 71,395 response candidates: 30,740 accepted, 27,104 rejected, and 13,551 strongly rejected. Three… See the full description on the dataset page: https://huggingface.co/datasets/KETI-NLP/KoEVD.niklDescription is **formatted** as markdown.
It should also contain any processing which has been applied (if any),
(e.g. corrupted example skipped, images cropped,...):K-prism
K-Prism
Korean diagnostic benchmark data for evaluating hallucination in vision-language
models. Evaluation code and protocol documentation are available at
alsgur0720/K-Prism.
Files
File
Contents
Text_track.json
504 text-track questions
Image_track.json
498 image-track questions
images/
165 original images referenced by the image track
The release contains 1,002 questions and approximately 303 MB of annotations and
images. Keep both JSON files… See the full description on the dataset page: https://huggingface.co/datasets/KETI-NLP/K-prism.KITD_SAMPLEkor_amazon_polarity
Dataset Card for amazon_polarity
Licensing Information
The data is distributed under the CC0 1.0 license.
Source Data Citation Information
McAuley, Julian, and Jure Leskovec. "Hidden factors and hidden topics: understanding rating dimensions with review text." In Proceedings of the 7th ACM conference on Recommender systems, pp. 165-172. 2013.
Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification.… See the full description on the dataset page: https://huggingface.co/datasets/KETI-NLP/kor_amazon_polarity.kor_qasc
Dataset Card for QASC
Licensing Information
The data is distributed under the CC BY 4.0 license.
Source Data Citation INformation
@article{allenai:qasc,
author = {Tushar Khot and Peter Clark and Michal Guerquin and Peter Jansen and Ashish Sabharwal},
title = {QASC: A Dataset for Question Answering via Sentence Composition},
journal = {arXiv:1910.11473v2},
year = {2020},
}