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
File size: 1,673 Bytes
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"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
}
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