diff --git a/test.py b/test.py
deleted file mode 100644
index 8e44e3f..0000000
--- a/test.py
+++ /dev/null
@@ -1,154 +0,0 @@
-from data_process import build_dataloader
-from model import TransClassifier
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from transformers import AutoTokenizer
-
-import pandas as pd
-import numpy as np
-import os
-import json
-from datetime import datetime
-import gc
-from tqdm import tqdm
-import warnings
-from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix
-warnings.filterwarnings("ignore")
-
-def test(backbone_dir, deal_folder, not_deal_folder, batch_size, ckpt_path="best_ckpt.pth", device="cuda"):
-    """
-    测试模型在测试集上的表现
-    
-    Args:
-        backbone_dir: 预训练模型目录
-        deal_folder: 成交数据文件夹
-        not_deal_folder: 非成交数据文件夹
-        batch_size: 批量大小
-        ckpt_path: 模型 checkpoint 路径
-        device: 运行设备
-    """
-    # 加载测试数据
-    data_dict = build_dataloader(deal_folder, not_deal_folder, batch_size)
-    test_loader = data_dict["test"]
-    print(f"Test data loaded successfully. Test samples: {len(test_loader.dataset)}")
-    
-    # 加载 tokenizer 和模型
-    tokenizer = AutoTokenizer.from_pretrained(backbone_dir)
-    model = TransClassifier(backbone_dir, device)
-    model.to(device)
-    
-    # 加载训练好的模型权重
-    if os.path.exists(ckpt_path):
-        model.load_state_dict(torch.load(ckpt_path, map_location=device))
-        print(f"Model loaded from {ckpt_path}")
-    else:
-        print(f"Warning: {ckpt_path} not found. Using untrained model.")
-    
-    # 测试模型
-    model.eval()
-    all_ids = []
-    all_preds = []
-    all_labels = []
-    test_loss = 0.0
-    
-    loss_func = nn.CrossEntropyLoss()
-    
-    pbar = tqdm(test_loader, desc="Testing")
-    with torch.inference_mode():
-        for batch_idx, (ids, texts, labels) in enumerate(pbar):
-            all_ids.extend(ids)
-            labels = labels.to(device)
-            
-            # 处理输入数据
-            texts = tokenizer.apply_chat_template(texts, tokenize=False, add_generation_prompt=True, enable_thinking=False)
-            inputs = tokenizer(texts, padding=True, truncation=True, max_length=2048, return_tensors="pt").to(device)
-            
-            with torch.amp.autocast(device_type="cuda", dtype=torch.bfloat16):
-                outputs = model(inputs)
-                loss = loss_func(outputs, labels)
-            
-            test_loss += loss.item()
-            
-            # 计算预测结果
-            preds = torch.argmax(outputs, dim=1).cpu().numpy()
-            all_preds.extend(preds)
-            all_labels.extend(labels.cpu().numpy())
-            
-            # 清理内存
-            del texts, labels, outputs, loss
-            torch.cuda.empty_cache()
-            gc.collect()
-    
-    # 计算评估指标
-    avg_loss = test_loss / len(test_loader)
-    accuracy = accuracy_score(all_labels, all_preds)
-    precision = precision_score(all_labels, all_preds, average="weighted")
-    recall = recall_score(all_labels, all_preds, average="weighted")
-    f1 = f1_score(all_labels, all_preds, average="weighted")
-    cm = confusion_matrix(all_labels, all_preds)
-    
-    # 打印评估结果
-    print("\n=== Test Results ===")
-    print(f"Average Loss: {avg_loss:.4f}")
-    print(f"Accuracy: {accuracy:.4f}")
-    print(f"Precision: {precision:.4f}")
-    print(f"Recall: {recall:.4f}")
-    print(f"F1 Score: {f1:.4f}")
-    print("\nConfusion Matrix:")
-    print(cm)
-    print("\n=== Class-wise Metrics ===")
-    print("Class 0 (Not Deal):")
-    print(f"  Precision: {precision_score(all_labels, all_preds, average=None)[0]:.4f}")
-    print(f"  Recall: {recall_score(all_labels, all_preds, average=None)[0]:.4f}")
-    print(f"  F1 Score: {f1_score(all_labels, all_preds, average=None)[0]:.4f}")
-    print("\nClass 1 (Deal):")
-    print(f"  Precision: {precision_score(all_labels, all_preds, average=None)[1]:.4f}")
-    print(f"  Recall: {recall_score(all_labels, all_preds, average=None)[1]:.4f}")
-    print(f"  F1 Score: {f1_score(all_labels, all_preds, average=None)[1]:.4f}")
-    
-    # 保存测试结果
-    test_results = {
-        "average_loss": avg_loss,
-        "accuracy": accuracy,
-        "precision": precision,
-        "recall": recall,
-        "f1_score": f1,
-        "confusion_matrix": cm.tolist(),
-        "class_0_precision": precision_score(all_labels, all_preds, average=None)[0],
-        "class_0_recall": recall_score(all_labels, all_preds, average=None)[0],
-        "class_0_f1": f1_score(all_labels, all_preds, average=None)[0],
-        "class_1_precision": precision_score(all_labels, all_preds, average=None)[1],
-        "class_1_recall": recall_score(all_labels, all_preds, average=None)[1],
-        "class_1_f1": f1_score(all_labels, all_preds, average=None)[1],
-        "test_samples": len(all_labels),
-        "timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-    }
-    
-    # 保存预测结果
-    pred_results = {
-        "ids": all_ids,
-        "predictions": all_preds,
-        "true_labels": all_labels
-    }
-    pred_df = pd.DataFrame(pred_results)
-    pred_df.to_csv("test_predictions.csv", index=False, encoding="utf-8")
-
-    # 保存为 JSON 文件
-    with open("test_results.json", "w", encoding="utf-8") as f:
-        json.dump(test_results, f, ensure_ascii=False, indent=2)
-    print("\nTest results saved to test_results.json")
-    return test_results
-
-if __name__ == "__main__":
-    # 配置参数
-    backbone_dir = r"C:\Users\GA\Desktop\models\Qwen3-1.7B"
-    deal_folder = "deal"
-    not_deal_folder = "not_deal"
-    batch_size = 8
-    ckpt_path = "best_ckpt.pth"
-    device = "cuda"
-    
-    # 运行测试
-    test(backbone_dir, deal_folder, not_deal_folder, batch_size, ckpt_path, device)
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