Text2TTP
Collection
A Series of Models used for the paper entitled: "Semantic Ranking for Automated Adversarial Technique Annotation in Security Text" • 4 items • Updated
How to use QCRI/monot5_AllDataSplit with sentence-transformers:
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]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
@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}
}