| | --- |
| | pipeline_tag: sentence-similarity |
| | tags: |
| | - sentence-transformers |
| | - feature-extraction |
| | - sentence-similarity |
| | - mitre_ttps |
| | - security |
| | - adversarial-threat-annotation |
| | --- |
| | |
| | # SentSecBert |
| |
|
| | This is a [sentence-transformers](https://www.SBERT.net) 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](https://github.com/qcri/Text2TTP) |
| |
|
| | ## Usage (Sentence-Transformers) |
| |
|
| | Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) 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} |
| | } |
| | ``` |
| |
|
| |
|
| |
|
