Text Classification
Transformers
PyTorch
distilbert
digital forensics
text-embeddings-inference
distilbert_class_heaps / config.json
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{
"_name_or_path": "johannes-garstenauer/distilbert_masking_heaps",
"activation": "gelu",
"architectures": [
"DistilBertForSequenceClassification"
],
"attention_dropout": 0.1,
"dim": 768,
"dropout": 0.1,
"hidden_dim": 3072,
"id2label": {
"0": "IRRELEVANT",
"1": "SESSION_STATE_ADDR",
"2": "NEWKEYS_ADDR",
"3": "ENCRYPTION_KEY_NAME_ADDR",
"4": "KEY_ADDR"
},
"initializer_range": 0.02,
"label2id": {
"ENCRYPTION_KEY_NAME_ADDR": 3,
"IRRELEVANT": 0,
"KEY_ADDR": 4,
"NEWKEYS_ADDR": 2,
"SESSION_STATE_ADDR": 1
},
"max_position_embeddings": 512,
"model_type": "distilbert",
"n_heads": 12,
"n_layers": 6,
"output_hidden_states": true,
"pad_token_id": 0,
"problem_type": "single_label_classification",
"qa_dropout": 0.1,
"seq_classif_dropout": 0.2,
"sinusoidal_pos_embds": false,
"torch_dtype": "float32",
"transformers_version": "4.34.0.dev0",
"vocab_size": 30005
}