Upload eval_pipeline.py

eval_pipeline.py

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+import pandas as pd
+from sklearn.model_selection import train_test_split
+from google.colab import drive
+import torch
+from torch.utils.data import Dataset, DataLoader
+from transformers import BertTokenizer, BertForSequenceClassification, AdamW
+from sklearn.metrics import accuracy_score, classification_report
+
+dataset_path = ""
+model_path = ""
+
+news_df = pd.read_csv(dataset_path)
+
+X = news_df['title']
+y = news_df['labels']
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)
+X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.25, random_state=1) # 0.25 x 0.8 = 0.2
+
+tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
+
+
+def tokenize_data(texts, tokenizer, max_len=128):
+    return tokenizer(
+        list(texts),
+        padding=True,
+        truncation=True,
+        max_length=max_len,
+        return_tensors="pt"
+    )
+
+# Tokenize the training and test datasets
+train_encodings = tokenize_data(X_train, tokenizer)
+test_encodings = tokenize_data(X_test, tokenizer)
+
+# Create a custom Dataset class
+class NewsDataset(Dataset):
+    def __init__(self, encodings, labels):
+        self.encodings = encodings
+        self.labels = labels
+
+    def __len__(self):
+        return len(self.labels)
+
+    def __getitem__(self, idx):
+        item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
+        item['labels'] = torch.tensor(self.labels[idx])
+        return item
+
+train_dataset = NewsDataset(train_encodings, y_train.tolist())
+test_dataset = NewsDataset(test_encodings, y_test.tolist())
+
+# Load DataLoader for batching
+train_loader = DataLoader(train_dataset, batch_size=16, shuffle=True)
+test_loader = DataLoader(test_dataset, batch_size=16)
+
+model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)
+model.load_state_dict(torch.load(model_path))
+
+device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
+model.to(device)
+
+# Define optimizer and scheduler
+# optimizer = AdamW(model.parameters(), lr=5e-5)
+# num_training_steps = len(train_loader) * 4  # Assume 4 epochs
+# lr_scheduler = get_scheduler("linear", optimizer=optimizer, num_warmup_steps=0, num_training_steps=num_training_steps)
+
+# Evaluate the model
+def evaluate_model(model, test_loader):
+    model.eval()
+    y_true, y_pred = [], []
+    with torch.no_grad():
+        for batch in test_loader:
+            batch = {k: v.to(device) for k, v in batch.items()}
+            outputs = model(**batch)
+            logits = outputs.logits
+            predictions = torch.argmax(logits, dim=-1)
+            y_true.extend(batch['labels'].tolist())
+            y_pred.extend(predictions.tolist())
+    return y_true, y_pred
+
+y_true, y_pred = evaluate_model(model, test_loader)
+
+# Print evaluation metrics
+print(f"Accuracy: {accuracy_score(y_true, y_pred):.4f}")
+print("Classification Report:\n", classification_report(y_true, y_pred))