Delete intent_classifier.py

intent_classifier.py

@@ -1,102 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.optim as optim
-from torch.utils.data import Dataset, DataLoader
-from transformers import BertTokenizer, BertForSequenceClassification
-from datasets import load_dataset
-from tqdm import tqdm
-from sklearn.metrics import accuracy_score, precision_recall_fscore_support
-
-# Check for CUDA
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-print(device)
-
-# Load CLINC-OOS Dataset (Correct Config)
-dataset = load_dataset("clinc_oos", "plus")
-
-# Tokenizer
-tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
-
-# Preprocess Dataset
-class IntentDataset(Dataset):
-    def __init__(self, dataset_split):
-        self.texts = dataset_split["text"]
-        self.labels = dataset_split["intent"]
-        self.label_map = {label: i for i, label in enumerate(set(self.labels))}  # Create label mapping
-
-    def __len__(self):
-        return len(self.texts)
-
-    def __getitem__(self, idx):
-        inputs = tokenizer(self.texts[idx], padding="max_length", truncation=True, max_length=64, return_tensors="pt")
-        label = self.labels[idx]
-        if label not in self.label_map:
-            raise ValueError(f"Unexpected label {label} found in dataset")  # Debugging step
-        return {key: val.squeeze(0) for key, val in inputs.items()}, torch.tensor(self.label_map[label])
-
-# Create Dataloaders
-batch_size = 16
-train_dataset = IntentDataset(dataset["train"])
-test_dataset = IntentDataset(dataset["test"])
-train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
-test_loader = DataLoader(test_dataset, batch_size=batch_size)
-
-# Load Pretrained BERT Model
-num_labels = len(set(dataset["train"]["intent"]))
-model = BertForSequenceClassification.from_pretrained("bert-base-uncased", num_labels=num_labels).to(device)
-
-# Loss & Optimizer
-criterion = nn.CrossEntropyLoss()
-optimizer = optim.AdamW(model.parameters(), lr=2e-5)
-
-# Training Loop
-num_epochs = 3
-for epoch in range(num_epochs):
-    model.train()
-    total_loss = 0
-    correct = 0
-    total = 0
-
-    for batch in tqdm(train_loader, desc=f"Epoch {epoch+1}/{num_epochs} Training"):
-        inputs, labels = batch
-        inputs = {key: val.to(device) for key, val in inputs.items()}
-        labels = labels.to(device)
-
-        optimizer.zero_grad()
-        outputs = model(**inputs).logits
-        loss = criterion(outputs, labels)
-        loss.backward()
-        optimizer.step()
-
-        total_loss += loss.item()
-        correct += (outputs.argmax(dim=1) == labels).sum().item()
-        total += labels.size(0)
-
-    train_accuracy = correct / total
-    print(f"Epoch {epoch+1}/{num_epochs}, Loss: {total_loss:.4f}, Train Accuracy: {train_accuracy:.4f}")
-
-# Evaluation on Test Set
-model.eval()
-all_preds, all_labels = [], []
-
-with torch.no_grad():
-    for batch in tqdm(test_loader, desc="Testing"):
-        inputs, labels = batch
-        inputs = {key: val.to(device) for key, val in inputs.items()}
-        labels = labels.to(device)
-
-        outputs = model(**inputs).logits
-        preds = outputs.argmax(dim=1)
-
-        all_preds.extend(preds.cpu().numpy())
-        all_labels.extend(labels.cpu().numpy())
-
-# Compute Metrics
-accuracy = accuracy_score(all_labels, all_preds)
-precision, recall, f1, _ = precision_recall_fscore_support(all_labels, all_preds, average="weighted")
-
-print(f"Test Accuracy: {accuracy:.4f}")
-print(f"Precision: {precision:.4f}, Recall: {recall:.4f}, F1-score: {f1:.4f}")
-
-# Save Model
-torch.save(model.state_dict(), "intent_classifier.pth")