Text Classification
Transformers
Safetensors
English
roberta
vulnerability
cybersecurity
security
cve
mitre-attack
attack-techniques
Generated from Trainer
text-embeddings-inference
Instructions to use CIRCL/vulnerability-attack-technique-classification-roberta-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CIRCL/vulnerability-attack-technique-classification-roberta-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="CIRCL/vulnerability-attack-technique-classification-roberta-base")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("CIRCL/vulnerability-attack-technique-classification-roberta-base") model = AutoModelForSequenceClassification.from_pretrained("CIRCL/vulnerability-attack-technique-classification-roberta-base") - Notebooks
- Google Colab
- Kaggle
| { | |
| "add_cross_attention": false, | |
| "architectures": [ | |
| "RobertaForSequenceClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "bos_token_id": 0, | |
| "classifier_dropout": null, | |
| "dtype": "float32", | |
| "eos_token_id": 2, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "id2label": { | |
| "0": "T1003", | |
| "1": "T1005", | |
| "2": "T1021", | |
| "3": "T1027", | |
| "4": "T1036", | |
| "5": "T1040", | |
| "6": "T1041", | |
| "7": "T1046", | |
| "8": "T1055", | |
| "9": "T1059", | |
| "10": "T1068", | |
| "11": "T1070", | |
| "12": "T1071", | |
| "13": "T1078", | |
| "14": "T1082", | |
| "15": "T1083", | |
| "16": "T1087", | |
| "17": "T1091", | |
| "18": "T1098", | |
| "19": "T1105", | |
| "20": "T1106", | |
| "21": "T1110", | |
| "22": "T1114", | |
| "23": "T1133", | |
| "24": "T1136", | |
| "25": "T1185", | |
| "26": "T1189", | |
| "27": "T1190", | |
| "28": "T1202", | |
| "29": "T1203", | |
| "30": "T1204", | |
| "31": "T1210", | |
| "32": "T1211", | |
| "33": "T1212", | |
| "34": "T1485", | |
| "35": "T1486", | |
| "36": "T1489", | |
| "37": "T1496", | |
| "38": "T1497", | |
| "39": "T1498", | |
| "40": "T1499", | |
| "41": "T1505", | |
| "42": "T1528", | |
| "43": "T1542", | |
| "44": "T1543", | |
| "45": "T1548", | |
| "46": "T1550", | |
| "47": "T1552", | |
| "48": "T1555", | |
| "49": "T1557", | |
| "50": "T1563", | |
| "51": "T1565", | |
| "52": "T1566", | |
| "53": "T1574", | |
| "54": "T1588", | |
| "55": "T1608", | |
| "56": "T1685" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "is_decoder": false, | |
| "label2id": { | |
| "T1003": 0, | |
| "T1005": 1, | |
| "T1021": 2, | |
| "T1027": 3, | |
| "T1036": 4, | |
| "T1040": 5, | |
| "T1041": 6, | |
| "T1046": 7, | |
| "T1055": 8, | |
| "T1059": 9, | |
| "T1068": 10, | |
| "T1070": 11, | |
| "T1071": 12, | |
| "T1078": 13, | |
| "T1082": 14, | |
| "T1083": 15, | |
| "T1087": 16, | |
| "T1091": 17, | |
| "T1098": 18, | |
| "T1105": 19, | |
| "T1106": 20, | |
| "T1110": 21, | |
| "T1114": 22, | |
| "T1133": 23, | |
| "T1136": 24, | |
| "T1185": 25, | |
| "T1189": 26, | |
| "T1190": 27, | |
| "T1202": 28, | |
| "T1203": 29, | |
| "T1204": 30, | |
| "T1210": 31, | |
| "T1211": 32, | |
| "T1212": 33, | |
| "T1485": 34, | |
| "T1486": 35, | |
| "T1489": 36, | |
| "T1496": 37, | |
| "T1497": 38, | |
| "T1498": 39, | |
| "T1499": 40, | |
| "T1505": 41, | |
| "T1528": 42, | |
| "T1542": 43, | |
| "T1543": 44, | |
| "T1548": 45, | |
| "T1550": 46, | |
| "T1552": 47, | |
| "T1555": 48, | |
| "T1557": 49, | |
| "T1563": 50, | |
| "T1565": 51, | |
| "T1566": 52, | |
| "T1574": 53, | |
| "T1588": 54, | |
| "T1608": 55, | |
| "T1685": 56 | |
| }, | |
| "layer_norm_eps": 1e-05, | |
| "max_position_embeddings": 514, | |
| "model_type": "roberta", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "pad_token_id": 1, | |
| "problem_type": "multi_label_classification", | |
| "tie_word_embeddings": true, | |
| "transformers_version": "5.13.0", | |
| "type_vocab_size": 1, | |
| "use_cache": false, | |
| "vocab_size": 50265 | |
| } | |