Create advanced_nlp.py

features/agi_financial_advisory/nlp/advanced_nlp.py

@@ -0,0 +1,61 @@
+# File name: advanced_nlp.py
+import torch
+import transformers
+
+# Load pre-trained language model
+model = transformers.BertForSequenceClassification.from_pretrained('bert-base-uncased')
+
+# Define custom dataset class
+class CustomDataset(torch.utils.data.Dataset):
+    def __init__(self, data, labels):
+        self.data = data
+        self.labels = labels
+
+    def __len__(self):
+        return len(self.data)
+
+    def __getitem__(self, idx):
+        text = self.data[idx]
+        label = self.labels[idx]
+        encoding = model.encode_plus(text, 
+                                      add_special_tokens=True, 
+                                      max_length=512, 
+                                      return_attention_mask=True, 
+                                      return_tensors='pt')
+        return {
+            'input_ids': encoding['input_ids'].flatten(),
+            'attention_mask': encoding['attention_mask'].flatten(),
+            'labels': torch.tensor(label, dtype=torch.long),
+        }
+
+# Load dataset
+data = [...]; // Load dataset
+labels = [...]; // Load labels
+
+# Create custom dataset instance
+dataset = CustomDataset(data, labels)
+
+# Create data loader
+data_loader = torch.utils.data.DataLoader(dataset, batch_size=32, shuffle=True)
+
+# Train model
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+model.to(device)
+criterion = torch.nn.CrossEntropyLoss()
+optimizer = torch.optim.Adam(model.parameters(), lr=1e-5)
+
+for epoch in range(5):
+    model.train()
+    total_loss = 0
+    for batch in data_loader:
+        input_ids = batch['input_ids'].to(device)
+        attention_mask = batch['attention_mask'].to(device)
+        labels = batch['labels'].to(device)
+        optimizer.zero_grad()
+        outputs = model(input_ids, attention_mask=attention_mask, labels=labels)
+        loss = criterion(outputs, labels)
+        loss.backward()
+        optimizer.step()
+        total_loss += loss.item()
+    print(f'Epoch {epoch+1}, Loss: {total_loss / len(data_loader)}')
+    model.eval()