Delete mood_classifier.py

mood_classifier.py

@@ -1,92 +0,0 @@
-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")