Text2TTP
Collection
A Series of Models used for the paper entitled: "Semantic Ranking for Automated Adversarial Technique Annotation in Security Text" • 4 items • Updated
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
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
Using this model becomes easy when you have sentence-transformers installed:
pip install -U sentence-transformers
Then you can use the model like this:
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('SentSecBert')
embeddings = model.encode(sentences)
print(embeddings)
@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}
}
from sentence_transformers import SentenceTransformer model = SentenceTransformer("QCRI/SentSecBert_10k_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]