Text2TTP
Collection
A Series of Models used for the paper entitled: "Semantic Ranking for Automated Adversarial Technique Annotation in Security Text" • 4 items • Updated
monot5_AllDataSplit
This model is a T5-base reranker.
This is a model used in our work "Semantic Ranking for Automated Adversarial Technique Annotation in Security Text". The code is available at: https://github.com/qcri/Text2TTP
Citation
@article{kumarasinghe2024semantic,
title={Semantic Ranking for Automated Adversarial Technique Annotation in Security Text},
author={Kumarasinghe, Udesh and Lekssays, Ahmed and Sencar, Husrev Taha and Boughorbel, Sabri and Elvitigala, Charitha and Nakov, Preslav},
journal={arXiv preprint arXiv:2403.17068},
year={2024}
}
- Downloads last month
- 10
from sentence_transformers import SentenceTransformer model = SentenceTransformer("QCRI/monot5_AllDataSplit") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4]