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
| { | |
| "architectures": [ | |
| "BertModel" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "classifier_dropout": null, | |
| "dtype": "float32", | |
| "gradient_checkpointing": false, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "id2label": { | |
| "0": "LABEL_0" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "label2id": { | |
| "LABEL_0": 0 | |
| }, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "bert", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "pad_token_id": 0, | |
| "position_embedding_type": "absolute", | |
| "transformers_version": "4.57.3", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 30522 | |
| } | |