Update README.md

README.md

@@ -4,14 +4,16 @@ 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.
 
-<!--- Describe your model here -->
+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)
 
@@ -32,25 +34,14 @@ embeddings = model.encode(sentences)
 print(embeddings)
 ```
 
-
-
-## Evaluation Results
-
-<!--- Describe how your model was evaluated -->
-
-For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=SentSecBert)
-
-
-
-## Full Model Architecture
+## Citation
 ```
-SentenceTransformer(
-  (0): Transformer({'max_seq_length': 514, 'do_lower_case': False}) with Transformer model: BertModel 
-  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
-  (2): Normalize()
-)
+@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}
+}
 ```
 
-## Citing & Authors
 
-<!--- Describe where people can find more information -->