import torch import torch.nn as nn import torch.optim as optim from transformers import BertTokenizer, BertForSequenceClassification from datasets import load_dataset from torch.utils.data import DataLoader, Dataset, random_split from tqdm import tqdm from sklearn.metrics import accuracy_score, precision_recall_fscore_support device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # Load GoEmotions dataset dataset = load_dataset("go_emotions", split="train") dataset = dataset.map(lambda x: {"label": x["labels"][0]}) # Convert multi-label to single-label labels = list(set(dataset["label"])) # Unique labels num_labels = len(labels) tokenizer = BertTokenizer.from_pretrained("bert-base-uncased") class MoodDataset(Dataset): def __init__(self, texts, labels): self.texts = texts self.labels = labels def __len__(self): return len(self.texts) def __getitem__(self, idx): inputs = tokenizer(self.texts[idx], return_tensors="pt", padding="max_length", truncation=True, max_length=128) return {key: val.squeeze(0) for key, val in inputs.items()}, torch.tensor(labels.index(self.labels[idx])) dataset = MoodDataset(dataset["text"], dataset["label"]) train_size = int(0.8 * len(dataset)) train_set, test_set = random_split(dataset, [train_size, len(dataset) - train_size]) train_loader = DataLoader(train_set, batch_size=32, shuffle=True) test_loader = DataLoader(test_set, batch_size=32) model = BertForSequenceClassification.from_pretrained("bert-base-uncased", num_labels=num_labels).to(device) optimizer = optim.AdamW(model.parameters(), lr=2e-5) criterion = nn.CrossEntropyLoss() num_epochs = 3 for epoch in range(num_epochs): model.train() epoch_loss, correct, total = 0, 0, 0 preds, labels_list = [], [] for batch in tqdm(train_loader, desc=f"Epoch {epoch+1}/{num_epochs} Training"): optimizer.zero_grad() inputs = {key: val.to(device) for key, val in batch[0].items()} labels = batch[1].to(device) outputs = model(**inputs).logits loss = criterion(outputs, labels) loss.backward() optimizer.step() epoch_loss += loss.item() correct += (outputs.argmax(dim=1) == labels).sum().item() total += labels.size(0) preds.extend(outputs.argmax(dim=1).cpu().numpy()) labels_list.extend(labels.cpu().numpy()) train_acc = accuracy_score(labels_list, preds) precision, recall, f1, _ = precision_recall_fscore_support(labels_list, preds, average="weighted") print(f"Epoch {epoch+1}: Loss: {epoch_loss:.4f}, Train Acc: {train_acc:.4f}, Precision: {precision:.4f}, Recall: {recall:.4f}, F1: {f1:.4f}") # **Evaluate on Test Set** model.eval() test_preds, test_labels = [], [] with torch.no_grad(): for batch in tqdm(test_loader, desc="Evaluating on Test Set"): inputs = {key: val.to(device) for key, val in batch[0].items()} labels = batch[1].to(device) outputs = model(**inputs).logits test_preds.extend(outputs.argmax(dim=1).cpu().numpy()) test_labels.extend(labels.cpu().numpy()) test_acc = accuracy_score(test_labels, test_preds) precision, recall, f1, _ = precision_recall_fscore_support(test_labels, test_preds, average="weighted") print(f"Test Accuracy: {test_acc:.4f}, Precision: {precision:.4f}, Recall: {recall:.4f}, F1-score: {f1:.4f}") # Save model model.save_pretrained("mood_classifier")