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pubmedbert finetuned model on caption and reference text of synthetic data
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{
"_name_or_path": "microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext",
"architectures": [
"BertForSequenceClassification"
],
"attention_probs_dropout_prob": 0.1,
"classifier_dropout": null,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"id2label": {
"0": "medical",
"1": "non-medical"
},
"initializer_range": 0.02,
"intermediate_size": 3072,
"label2id": {
"false": 0,
"true": 1
},
"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",
"problem_type": "single_label_classification",
"torch_dtype": "float32",
"transformers_version": "4.42.4",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 30522
}