Fraud-Detection_BERT_grado / fraud_detector.py
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Update 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()
except Exception as e:
return f"Error: {str(e)}"