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ai.py

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+# ai.py
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.utils.data import DataLoader
+from sklearn.model_selection import train_test_split
+from datasets import load_dataset
+from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
+from tqdm import tqdm
+
+# Load IMDb dataset
+dataset = load_dataset("imdb")
+texts, labels = dataset["train"]["text"], dataset["train"]["label"]
+
+# Split the dataset into training and validation sets
+train_texts, val_texts, train_labels, val_labels = train_test_split(texts, labels, test_size=0.1, random_state=42)
+
+# Tokenize and preprocess the data
+tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased")
+train_encodings = tokenizer(train_texts, truncation=True, padding=True, return_tensors="pt", max_length=256)
+val_encodings = tokenizer(val_texts, truncation=True, padding=True, return_tensors="pt", max_length=256)
+
+# Define Sentiment Analysis Model
+class SentimentAnalysisModel(nn.Module):
+    def __init__(self):
+        super(SentimentAnalysisModel, self).__init__()
+        self.distilbert = DistilBertForSequenceClassification.from_pretrained("distilbert-base-uncased", num_labels=2)
+
+    def forward(self, input_ids, attention_mask):
+        return self.distilbert(input_ids, attention_mask=attention_mask).logits
+
+# Initialize model, criterion, and optimizer
+model = SentimentAnalysisModel()
+criterion = nn.CrossEntropyLoss()
+optimizer = optim.AdamW(model.parameters(), lr=5e-5)
+
+# Convert labels to tensor
+train_labels = torch.tensor(train_labels)
+val_labels = torch.tensor(val_labels)
+
+# Prepare DataLoader
+train_dataset = torch.utils.data.TensorDataset(train_encodings["input_ids"], train_encodings["attention_mask"], train_labels)
+train_loader = DataLoader(train_dataset, batch_size=8, shuffle=True)
+
+val_dataset = torch.utils.data.TensorDataset(val_encodings["input_ids"], val_encodings["attention_mask"], val_labels)
+val_loader = DataLoader(val_dataset, batch_size=8, shuffle=False)
+
+# Train the model
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+
+num_epochs = 5  # Increase the number of epochs
+for epoch in range(num_epochs):
+    model.train()
+    total_loss = 0.0
+
+    for input_ids, attention_mask, labels in tqdm(train_loader, desc=f"Epoch {epoch + 1}/{num_epochs}"):
+        input_ids, attention_mask, labels = input_ids.to(device), attention_mask.to(device), labels.to(device)
+
+        optimizer.zero_grad()
+        outputs = model(input_ids, attention_mask=attention_mask)
+        loss = criterion(outputs, labels)
+        loss.backward()
+        optimizer.step()
+
+        total_loss += loss.item()
+
+    print(f"Epoch {epoch + 1}/{num_epochs}, Average Loss: {total_loss / len(train_loader)}")
+
+# Save the trained model
+torch.save(model.state_dict(), "sentiment_analysis_model.pth")