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
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.models.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
| }, | |
| { | |
| "idx": 2, | |
| "name": "2", | |
| "path": "2_Normalize", | |
| "type": "sentence_transformers.models.Normalize" | |
| } | |
| ] |