|
|
| """
|
| 文本编码器模块：使用PubMedBERT对药物治疗文本进行编码
|
| """
|
| import torch
|
| import torch.nn as nn
|
| from transformers import AutoTokenizer, AutoModel
|
|
|
| class DrugTextEncoder(nn.Module): |
| """
|
| 使用PubMedBERT编码药物治疗文本
|
| 支持冻结/微调模式
|
| """
|
| def __init__( |
| self, |
| model_name: str = "microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext", |
| freeze: bool = False, |
| output_dim: int = 768, |
| max_length: int = 512, |
| use_pooler_output: bool = True, |
| ): |
| super().__init__() |
| self.max_length = max_length |
| self.use_pooler_output = use_pooler_output |
|
|
|
|
| self.tokenizer = AutoTokenizer.from_pretrained(model_name)
|
| self.bert = AutoModel.from_pretrained(model_name)
|
|
|
|
|
| if freeze:
|
| for param in self.bert.parameters():
|
| param.requires_grad = False
|
|
|
|
|
| self.projection = None
|
| if output_dim != 768:
|
| self.projection = nn.Linear(768, output_dim)
|
|
|
| self.output_dim = output_dim
|
|
|
| def forward(self, texts):
|
| """
|
| Args:
|
| texts: List[str] or str, 药物治疗的JSON字符串
|
|
|
| Returns:
|
| embeddings: [B, output_dim]
|
| """
|
|
|
| if isinstance(texts, str):
|
| texts = [texts]
|
|
|
|
|
| encoded = self.tokenizer(
|
| texts,
|
| padding=True,
|
| truncation=True,
|
| max_length=self.max_length,
|
| return_tensors='pt'
|
| )
|
|
|
|
|
| device = next(self.bert.parameters()).device
|
| encoded = {k: v.to(device) for k, v in encoded.items()}
|
|
|
| |
| outputs = self.bert(**encoded) |
| |
| |
| if self.use_pooler_output and getattr(outputs, "pooler_output", None) is not None: |
| cls_embeddings = outputs.pooler_output |
| else: |
| cls_embeddings = outputs.last_hidden_state[:, 0, :] |
|
|
|
|
| if self.projection is not None:
|
| cls_embeddings = self.projection(cls_embeddings)
|
|
|
| return cls_embeddings
|
|
|
| def unfreeze(self):
|
| """解冻BERT参数，用于微调"""
|
| for param in self.bert.parameters():
|
| param.requires_grad = True
|
|
|
| def freeze(self):
|
| """冻结BERT参数"""
|
| for param in self.bert.parameters():
|
| param.requires_grad = False
|
|
|
|
|
|
|
| if __name__ == "__main__":
|
| encoder = DrugTextEncoder(freeze=True)
|
|
|
|
|
| sample_text = '{"pre":{"tp":"T1","therapies":[{"type":"chemotherapy","agents":[{"name":"Temozolomide","dose":"75mg/m2"}]}]}}'
|
|
|
| embedding = encoder([sample_text, sample_text])
|
| print(f"Output shape: {embedding.shape}") |
|
|
