Sentence Similarity
sentence-transformers
PyTorch
t5
feature-extraction
mitre_ttps
security
adversarial-threat-annotation
Instructions to use QCRI/monot5_AllDataSplit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
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] - Notebooks
- Google Colab
- Kaggle
| pipeline_tag: sentence-similarity | |
| tags: | |
| - sentence-transformers | |
| - feature-extraction | |
| - sentence-similarity | |
| - mitre_ttps | |
| - security | |
| - adversarial-threat-annotation | |
| ## 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](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} | |
| } | |
| ``` |