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from transformers import BertModel
import torch
from .configuration_mcqbert import MCQBertConfig
class MCQStudentBert(BertModel):
config_class = MCQBertConfig
def __init__(self, config: MCQBertConfig):
super().__init__(config)
if config.integration_strategy is not None:
self.student_embedding_layer = torch.nn.Linear(config.student_embedding_size, config.hidden_size)
cls_input_dim_multiplier = 2 if config.integration_strategy == "cat" else 1
cls_input_dim = self.config.hidden_size * cls_input_dim_multiplier
self.classifier = torch.nn.Sequential(
torch.nn.Linear(cls_input_dim, config.cls_hidden_size),
torch.nn.ReLU(),
torch.nn.Linear(config.cls_hidden_size, 1)
)
def forward(self, input_ids, student_embeddings=None):
if self.config.integration_strategy is None:
# don't consider embeddings is no integration strategy (MCQBert)
output = super().forward(input_ids)
return self.classifier(output.last_hidden_state[:, 0, :])
elif self.config.integration_strategy == "cat":
# MCQStudentBertCat
output = super().forward(input_ids)
output_with_student_embedding = torch.cat((output.last_hidden_state[:, 0, :], self.student_embedding_layer(student_embeddings).unsqueeze(0)), dim = 1)
return self.classifier(output_with_student_embedding)
elif self.config.integration_strategy == "sum":
# MCQStudentBertSum
input_embeddings = self.embeddings(input_ids)
combined_embeddings = input_embeddings + self.student_embedding_layer(student_embeddings).repeat(1, input_embeddings.size(1), 1)
output = super().forward(inputs_embeds = combined_embeddings)
return self.classifier(output.last_hidden_state[:, 0, :])
else:
raise ValueError(f"{self.config.integration_strategy} is not a known integration_strategy") |