| import torch |
| import torch.nn as nn |
| from transformers import BertModel |
|
|
| class PersonaAssigner(nn.Module): |
| def __init__(self, input_dim, hidden_dim, output_dim): |
| super(PersonaAssigner, self).__init__() |
| self.fc1 = nn.Linear(input_dim, hidden_dim) |
| self.fc2 = nn.Linear(hidden_dim, output_dim) |
| |
| def forward(self, x): |
| x = torch.relu(self.fc1(x)) |
| return self.fc2(x) |
|
|
| class PreferencePredictor(nn.Module): |
| def __init__(self, input_dim): |
| super(PreferencePredictor, self).__init__() |
| self.fc1 = nn.Linear(input_dim, 256) |
| self.fc2 = nn.Linear(256, 3) |
| |
| def forward(self, x): |
| x = torch.relu(self.fc1(x)) |
| return self.fc2(x) |
|
|
| class BERTEncoder(nn.Module): |
| def __init__(self, model_name='bert-base-uncased'): |
| super(BERTEncoder, self).__init__() |
| self.bert = BertModel.from_pretrained(model_name) |
| |
| def forward(self, input_ids, attention_mask, token_type_ids=None): |
| outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids) |
| return outputs.last_hidden_state.mean(dim=1) |
