Sentence Similarity
Transformers
Safetensors
English
roberta
feature-extraction
security
vulnerability
mitre-attack
cve
bi-encoder
text-embeddings-inference
Instructions to use CIRCL/vulnerability-attack-technique-biencoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CIRCL/vulnerability-attack-technique-biencoder with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("CIRCL/vulnerability-attack-technique-biencoder") model = AutoModel.from_pretrained("CIRCL/vulnerability-attack-technique-biencoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "add_cross_attention": false, | |
| "architectures": [ | |
| "RobertaModel" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "biencoder": { | |
| "holdout_techniques": [], | |
| "labels": [ | |
| "T1003", | |
| "T1005", | |
| "T1021", | |
| "T1036", | |
| "T1040", | |
| "T1041", | |
| "T1046", | |
| "T1055", | |
| "T1059", | |
| "T1068", | |
| "T1070", | |
| "T1071", | |
| "T1078", | |
| "T1082", | |
| "T1083", | |
| "T1087", | |
| "T1091", | |
| "T1098", | |
| "T1105", | |
| "T1106", | |
| "T1110", | |
| "T1133", | |
| "T1136", | |
| "T1185", | |
| "T1189", | |
| "T1190", | |
| "T1202", | |
| "T1203", | |
| "T1204", | |
| "T1210", | |
| "T1211", | |
| "T1212", | |
| "T1485", | |
| "T1486", | |
| "T1496", | |
| "T1497", | |
| "T1498", | |
| "T1499", | |
| "T1505", | |
| "T1528", | |
| "T1542", | |
| "T1543", | |
| "T1548", | |
| "T1550", | |
| "T1552", | |
| "T1555", | |
| "T1557", | |
| "T1563", | |
| "T1565", | |
| "T1566", | |
| "T1574", | |
| "T1608", | |
| "T1685" | |
| ], | |
| "logit_bias": -4.998987674713135, | |
| "logit_scale": 10.00108814239502, | |
| "technique_max_length": 256 | |
| }, | |
| "bos_token_id": 0, | |
| "classifier_dropout": null, | |
| "dtype": "float32", | |
| "eos_token_id": 2, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "is_decoder": false, | |
| "layer_norm_eps": 1e-05, | |
| "max_position_embeddings": 514, | |
| "metadata_inputs": [], | |
| "model_type": "roberta", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "pad_token_id": 1, | |
| "tie_word_embeddings": true, | |
| "transformers_version": "5.15.0", | |
| "type_vocab_size": 1, | |
| "use_cache": false, | |
| "vocab_size": 50265 | |
| } | |