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

Usage (Sentence-Transformers)

Using this model becomes easy when you have sentence-transformers installed:

pip install -U sentence-transformers

Then you can use the model like this:

python

from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]

model = SentenceTransformer('SentSecBert')
embeddings = model.encode(sentences)
print(embeddings)

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}
}

qcri-cs/SentSecBert_10k_AllDataSplit

SentSecBert_10k_AllDataSplit

Usage (Sentence-Transformers)

Citation

qcri-cs/
SentSecBert_10k_AllDataSplit