Upload model.py

model.py

@@ -1,35 +1,49 @@
 import torch
 from transformers import BertModel
 
+
 class Ensembler(torch.nn.Module):
     def __init__(self, specialists):
         super().__init__()
-
         self.specialists = specialists
 
     def forward(self, input_ids, attention_mask):
-        outputs = torch.cat([specialist(input_ids, attention_mask)
-                             for specialist in self.specialists], dim=1)
+        outputs = []
+
+        for specialist in self.specialists:
+            specialist.eval()
+
+            specialist.to(torch.device(
+                "cuda" if torch.cuda.is_available() else "cpu"))
+
+            outputs.append(specialist(input_ids, attention_mask))
+
+            # Remove the specialist from the GPU
+            specialist.cpu()
+
+        outputs = torch.cat(outputs, dim=1)
 
         return torch.mean(outputs, dim=1).unsqueeze(1)
-        
+
 
 class LanguageIdentifier(torch.nn.Module):
     def __init__(self):
         super().__init__()
-        
-        self.portuguese_bert = BertModel.from_pretrained("neuralmind/bert-large-portuguese-cased")
-        
+
+        self.portuguese_bert = BertModel.from_pretrained(
+            "neuralmind/bert-large-portuguese-cased")
+
         self.linear_layer = torch.nn.Sequential(
             torch.nn.Dropout(p=0.2),
             torch.nn.Linear(self.portuguese_bert.config.hidden_size, 1),
         )
 
     def forward(self, input_ids, attention_mask):
-        
-        #(Batch_Size,Sequence Length, Hidden_Size)
-        outputs = self.portuguese_bert(input_ids=input_ids, attention_mask=attention_mask).last_hidden_state[:, 0, :]
-        
+
+        # (Batch_Size,Sequence Length, Hidden_Size)
+        outputs = self.portuguese_bert(
+            input_ids=input_ids, attention_mask=attention_mask).last_hidden_state[:, 0, :]
+
         outputs = self.linear_layer(outputs)
 
-        return outputs 
+        return outputs