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| 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") | |