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import torch
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from torch import nn
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from transformers import AutoModel
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class CustomModel(torch.nn.Module):
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def __init__(self, model_path, dropout=0.1, hidden_dim=768):
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super().__init__()
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self.metric_names = ['valence', 'arousal', 'dominance', 'aoa', 'concreteness']
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self.dropout_rate = dropout
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self.hidden_dim = hidden_dim
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self.bert = AutoModel.from_pretrained(model_path)
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for name in self.metric_names:
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setattr(self, name, nn.Linear(hidden_dim, 1))
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setattr(self, 'l_1_' + name, nn.Linear(hidden_dim, hidden_dim))
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self.layer_norm = nn.LayerNorm(self.hidden_dim)
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self.relu = nn.ReLU()
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self.dropout = nn.Dropout(self.dropout_rate)
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self.sigmoid = nn.Sigmoid()
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def save_pretrained(self, save_directory):
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self.bert.save_pretrained(save_directory)
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torch.save(self.state_dict(), f'{save_directory}/pytorch_model.bin')
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@classmethod
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def from_pretrained(cls, model_dir, dropout=0.2, hidden_dim=768):
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model = cls(model_dir, dropout, hidden_dim)
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state_dict = torch.load(f'{model_dir}/pytorch_model.bin', map_location=torch.device('cpu'))
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model.load_state_dict(state_dict)
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return model
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def forward(self, *args):
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_, x = self.bert(*args, return_dict=False)
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output = self.rate_embedding(x)
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return output
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def rate_embedding(self, x):
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output_ratings = []
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for name in self.metric_names:
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first_layer = self.relu(self.dropout(self.layer_norm(getattr(self, 'l_1_' + name)(x) + x)))
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second_layer = self.sigmoid(getattr(self, name)(first_layer))
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output_ratings.append(second_layer)
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return output_ratings |
