Upload model_script.py

model_script.py

@@ -0,0 +1,58 @@
+import torch
+import torch.nn as nn
+from transformers import AutoModel
+
+
+class Model(torch.nn.Module):
+
+    def __init__(self, model_dir, dropout=0.2, hidden_dim=768):
+        """
+        Initialize the model.
+        :param model_name: the name of the model
+        :param metric_names: the names of the metrics to use
+        :param dropout: the dropout rate
+        :param hidden_dim: the hidden dimension of the model
+        """
+        super(Model, self).__init__()
+        self.metric_names = ['Happiness', 'Sadness', 'Anger', 'Disgust', 'Fear', 'Pride', 'Valence', 'Arousal']
+        self.bert = AutoModel.from_pretrained(model_dir)
+
+
+        for name in self.metric_names:
+            setattr(self, name, nn.Linear(hidden_dim, 1))
+            setattr(self, 'l_1_' + name, nn.Linear(hidden_dim, hidden_dim))
+
+        self.layer_norm = nn.LayerNorm(hidden_dim)
+        self.relu = nn.ReLU()
+        self.dropout = nn.Dropout(dropout)
+        self.sigmoid = nn.Sigmoid()
+
+    def forward(self, input_id, mask):
+        """
+        Forward pass of the model.
+        :param args: the inputs
+        :return: the outputs
+        """
+        _, x = self.bert(input_ids = input_id, attention_mask=mask, return_dict=False)
+        output = self.rate_embedding(x)
+        return output
+
+    def rate_embedding(self, x):
+        output_ratings = []
+        for name in self.metric_names:
+            first_layer = self.relu(self.dropout(self.layer_norm(getattr(self, 'l_1_' + name)(x) + x)))
+            second_layer = self.sigmoid(getattr(self, name)(first_layer))
+            output_ratings.append(second_layer)
+
+        return output_ratings
+
+    def save_pretrained(self, save_directory):
+        self.bert.save_pretrained(save_directory)
+        torch.save(self.state_dict(), f'{save_directory}/pytorch_model.bin')
+
+    @classmethod
+    def from_pretrained(cls, model_dir, dropout=0.2, hidden_dim=768):
+        model = cls(model_dir, dropout, hidden_dim)
+        state_dict = torch.load(f'{model_dir}/pytorch_model.bin', map_location=torch.device('cpu'))
+        model.load_state_dict(state_dict)
+        return model