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import torch
import torch.nn as nn
from transformers import PreTrainedModel, AutoModel
from .model_config import CustomConfig
class LogRegClassifier(nn.Module):
def __init__(self, transformer_output_dim):
super(LogRegClassifier, self).__init__()
self.linear = nn.Linear(transformer_output_dim, 1)
def forward(self, x):
return torch.sigmoid(self.linear(x))
class CombinedModel(PreTrainedModel):
config_class = CustomConfig
def __init__(self, config):
super().__init__(config)
self.transformer = AutoModel.from_pretrained(config.transformer_type)
self.classifier = LogRegClassifier(config.transformer_output_dim)
def forward(self, input_ids, attention_mask):
outputs = self.transformer(input_ids=input_ids, attention_mask=attention_mask)
pooled_output = outputs.last_hidden_state[:, 0, :]
return self.classifier(pooled_output)
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