Create fraud_detector.py

fraud_detector.py

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+# fraud_detector.py
+
+import torch
+import pandas as pd
+from transformers import BertTokenizer, BertForSequenceClassification
+from torch.utils.data import Dataset
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score, precision_recall_fscore_support
+
+class FinancialFraudDataset(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
+
+class FinancialFraudTrainer:
+    def __init__(self, data_path=None):
+        self.data_path = data_path
+        self.tokenizer = None
+        self.model = None
+
+    def load_model(self):
+        # 從 Hugging Face 模型倉庫載入（或用你訓練好的模型路徑）
+        self.model = BertForSequenceClassification.from_pretrained("hfl/chinese-roberta-wwm-ext", num_labels=2)
+        self.tokenizer = BertTokenizer.from_pretrained("hfl/chinese-roberta-wwm-ext")
+        self.model.eval()
+
+    def predict_transaction(self, text):
+        try:
+            self.model.eval()
+            inputs = self.tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=128)
+            with torch.no_grad():
+                outputs = self.model(**inputs)
+                probs = torch.softmax(outputs.logits, dim=1)
+                prediction = torch.argmax(probs, dim=1).item()
+                confidence = probs[0][prediction].item()