Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:54320
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use Andou2yu/deep-attackg-bi-encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Andou2yu/deep-attackg-bi-encoder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Andou2yu/deep-attackg-bi-encoder") sentences = [ "The ransomware injected malicious.dll into svchost.exe to execute its encryption routines under a trusted process.", "A rootkit modified UEFI runtime services to maintain control over the system.", "ZxxZ has relied on victims to open a malicious attachment delivered via email.", "FIN7 has used random junk code to obfuscate malware code." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| { | |
| "__version__": { | |
| "sentence_transformers": "5.2.0", | |
| "transformers": "4.57.3", | |
| "pytorch": "2.9.1" | |
| }, | |
| "model_type": "SentenceTransformer", | |
| "prompts": { | |
| "query": "", | |
| "document": "" | |
| }, | |
| "default_prompt_name": null, | |
| "similarity_fn_name": "cosine" | |
| } |