| | 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: |
| | |
| | output = super().forward(input_ids) |
| | return self.classifier(output.last_hidden_state[:, 0, :]) |
| |
|
| | elif self.config.integration_strategy == "cat": |
| | |
| | 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": |
| | |
| | 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") |