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import torch
import torch.nn as nn
from transformers import AutoModel
class MultitaskCodeSimilarityModel(nn.Module):
def __init__(self, config, tokenizer):
super().__init__()
self.config = config
self.tokenizer = tokenizer
self.encoder = AutoModel.from_config(config)
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
# For explanation generation
self.decoder_embedding = nn.Linear(config.hidden_size, config.hidden_size)
self.decoder = nn.GRU(
input_size=config.hidden_size,
hidden_size=config.hidden_size,
batch_first=True
)
self.explanation_head = nn.Linear(config.hidden_size, len(tokenizer))
def forward(self, input_ids, attention_mask, explanation_ids=None, explanation_mask=None):
outputs = self.encoder(input_ids=input_ids, attention_mask=attention_mask)
pooled = outputs.last_hidden_state[:, 0]
logits = self.classifier(pooled)
explanation_logits = None
if explanation_ids is not None:
decoder_input = self.decoder_embedding(pooled).unsqueeze(1).expand(-1, explanation_ids.size(1), -1)
decoder_outputs, _ = self.decoder(decoder_input)
explanation_logits = self.explanation_head(decoder_outputs)
return logits, explanation_logits |