Update README.md

README.md

@@ -1,89 +1,2 @@
 ## Evaluation Pipeline
-# Use the raw version of the text below to evaluate the model. Make sure to set the dataset and model path.
-
-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))
+# Use eval_pipeline.py to evaluate the model. Make sure to set the dataset and model path.