|
|
| import torch |
| from transformers import PreTrainedModel |
|
|
| class BiEncoderModelRegression(torch.nn.Module): |
| def __init__(self, base_model, config=None, loss_fn="mse"): |
| super().__init__() |
| self.base_model = base_model |
| self.cos = torch.nn.CosineSimilarity(dim=1) |
| self.loss_fn = loss_fn |
| self.config = config |
|
|
| def forward(self, input_ids_text1, attention_mask_text1, input_ids_text2, attention_mask_text2, labels=None): |
| outputs_text1 = self.base_model(input_ids_text1, attention_mask=attention_mask_text1) |
| outputs_text2 = self.base_model(input_ids_text2, attention_mask=attention_mask_text2) |
| |
| cls_embedding_text1 = outputs_text1.last_hidden_state[:, 0, :] |
| cls_embedding_text2 = outputs_text2.last_hidden_state[:, 0, :] |
| |
| cos_sim = self.cos(cls_embedding_text1, cls_embedding_text2) |
| |
| loss = None |
| if labels is not None: |
| if self.loss_fn == "mse": |
| loss_fct = torch.nn.MSELoss() |
| elif self.loss_fn == "mae": |
| loss_fct = torch.nn.L1Loss() |
| elif self.loss_fn == "cosine_embedding": |
| loss_fct = torch.nn.CosineEmbeddingLoss() |
| labels_cosine = 2 * (labels > 0.5).float() - 1 |
| return {"loss": loss_fct(cls_embedding_text1, cls_embedding_text2, labels_cosine), "logits": cos_sim} |
| |
| loss = loss_fct(cos_sim, labels) |
| |
| return {"loss": loss, "logits": cos_sim} |
|
|
