Upload 4 files

app.py

@@ -0,0 +1,6 @@
+import subprocess
+import sys
+
+print("🚀 開始執行訓練腳本...")
+result = subprocess.run([sys.executable, "train.py"], capture_output=False)
+sys.exit(result.returncode)

requirements.txt

@@ -0,0 +1,12 @@
+transformers>=4.45.0
+torch>=2.0.0
+gradio>=4.44.1
+peft>=0.12.0
+accelerate>=0.34.0
+bitsandbytes>=0.43.2
+sentencepiece>=0.2.0
+protobuf>=3.20.0
+scikit-learn>=1.4.0
+datasets>=2.18.0
+pandas>=2.2.0
+huggingface_hub>=0.20.0

train.py

@@ -0,0 +1,536 @@
+"""
+Llama NBCD Fine-tuning Script with Baseline Comparison
+比較未微調 vs 微調模型的效果
+"""
+
+import pandas as pd
+import torch
+from datasets import Dataset, DatasetDict
+from transformers import (
+    AutoTokenizer,
+    AutoModelForSequenceClassification,
+    TrainingArguments,
+    Trainer,
+    DataCollatorWithPadding
+)
+from peft import LoraConfig, get_peft_model, TaskType
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score, precision_recall_fscore_support
+from sklearn.utils import resample
+import numpy as np
+import json
+from datetime import datetime
+import os
+from huggingface_hub import login
+
+# ==================== HF Token 登入 ====================
+print("🔐 檢查 Hugging Face Token...")
+if "HF_TOKEN" in os.environ:
+    try:
+        login(token=os.environ["HF_TOKEN"])
+        print("✅ 已使用 HF Token 登入")
+    except Exception as e:
+        print(f"⚠️ Token 登入失敗: {e}")
+else:
+    print("⚠️ 未找到 HF_TOKEN,可能無法下載 Llama 模型")
+
+# ==================== 配置參數 ====================
+MODEL_NAME = "meta-llama/Llama-3.2-1B"
+TRAINING_DATA_PATH = "./training_data.csv"
+OUTPUT_DIR = "./trained_model"
+MAX_LENGTH = 512
+
+# 訓練參數
+TRAIN_CONFIG = {
+    "num_epochs": 3,
+    "batch_size": 4,
+    "learning_rate": 1e-4,
+    "lora_r": 8,
+    "lora_alpha": 16,
+}
+
+# 資料平衡配置
+BALANCE_CONFIG = {
+    "target_samples_per_class": 700,
+    "use_class_weights": True,
+}
+
+print("\n" + "="*70)
+print("🦙 Llama NBCD Fine-tuning with Baseline Comparison")
+print("   (未微調 vs 微調模型比較)")
+print("="*70)
+print(f"\n📋 配置:")
+print(f"  模型: {MODEL_NAME}")
+print(f"  訓練數據: {TRAINING_DATA_PATH}")
+print(f"  輸出目錄: {OUTPUT_DIR}")
+print(f"  Epochs: {TRAIN_CONFIG['num_epochs']}")
+print(f"  Batch Size: {TRAIN_CONFIG['batch_size']}")
+print(f"  Learning Rate: {TRAIN_CONFIG['learning_rate']}")
+print(f"  目標樣本數: {BALANCE_CONFIG['target_samples_per_class']} 筆/類別")
+print("="*70 + "\n")
+
+# ==================== 1. 載入數據 ====================
+print("📂 載入訓練數據...")
+try:
+    df = pd.read_csv(TRAINING_DATA_PATH)
+    print(f"✅ 成功載入 {len(df)} 筆數據")
+    print(f"   欄位: {list(df.columns)}")
+    print(f"   原始 Class 0: {(df['nbcd']==0).sum()} 筆")
+    print(f"   原始 Class 1: {(df['nbcd']==1).sum()} 筆")
+except Exception as e:
+    print(f"❌ 無法載入數據: {e}")
+    print(f"   請確認 {TRAINING_DATA_PATH} 存在且格式正確")
+    exit(1)
+
+# ==================== 2. 資料平衡處理 ====================
+print("\n⚖️ 執行資料平衡...")
+
+df_class_0 = df[df['nbcd'] == 0]
+df_class_1 = df[df['nbcd'] == 1]
+
+target_n = BALANCE_CONFIG['target_samples_per_class']
+
+# 欠採樣 Class 0
+if len(df_class_0) > target_n:
+    df_class_0_balanced = resample(df_class_0, n_samples=target_n, random_state=42, replace=False)
+    print(f"✅ Class 0 欠採樣: {len(df_class_0)} → {len(df_class_0_balanced)} 筆")
+else:
+    df_class_0_balanced = df_class_0
+    print(f"⚠️ Class 0 樣本數不足,保持 {len(df_class_0)} 筆")
+
+# 過採樣 Class 1
+if len(df_class_1) < target_n:
+    df_class_1_balanced = resample(df_class_1, n_samples=target_n, random_state=42, replace=True)
+    print(f"✅ Class 1 過採樣: {len(df_class_1)} → {len(df_class_1_balanced)} 筆")
+else:
+    df_class_1_balanced = df_class_1
+    print(f"⚠️ Class 1 樣本數充足,保持 {len(df_class_1)} 筆")
+
+df_balanced = pd.concat([df_class_0_balanced, df_class_1_balanced])
+df_balanced = df_balanced.sample(frac=1, random_state=42).reset_index(drop=True)
+
+print(f"\n📊 平衡後數據:")
+print(f"   總樣本數: {len(df_balanced)} 筆")
+print(f"   Class 0: {(df_balanced['nbcd']==0).sum()} 筆")
+print(f"   Class 1: {(df_balanced['nbcd']==1).sum()} 筆")
+
+# ==================== 3. 計算類別權重 ====================
+if BALANCE_CONFIG['use_class_weights']:
+    print("\n⚖️ 計算類別權重...")
+    class_counts = df_balanced['nbcd'].value_counts().sort_index()
+    total = len(df_balanced)
+    num_classes = 2
+
+    class_weight_0 = total / (num_classes * class_counts[0])
+    class_weight_1 = total / (num_classes * class_counts[1])
+    class_weights = torch.tensor([class_weight_0, class_weight_1], dtype=torch.float32)
+
+    print(f"✅ 類別權重計算完成:")
+    print(f"   Class 0 權重: {class_weight_0:.4f}")
+    print(f"   Class 1 權重: {class_weight_1:.4f}")
+else:
+    class_weights = None
+    print("\n⚠️ 未使用類別權重")
+
+# ==================== 4. 分割數據 ====================
+print("\n✂️ 分割訓練集和測試集...")
+train_df, test_df = train_test_split(
+    df_balanced,
+    test_size=0.2,
+    stratify=df_balanced['nbcd'],
+    random_state=42
+)
+print(f"✅ 訓練集: {len(train_df)} 筆 (Class 0: {(train_df['nbcd']==0).sum()}, Class 1: {(train_df['nbcd']==1).sum()})")
+print(f"✅ 測試集: {len(test_df)} 筆 (Class 0: {(test_df['nbcd']==0).sum()}, Class 1: {(test_df['nbcd']==1).sum()})")
+
+dataset = DatasetDict({
+    'train': Dataset.from_pandas(train_df[['Text', 'nbcd']]),
+    'test': Dataset.from_pandas(test_df[['Text', 'nbcd']])
+})
+
+# ==================== 5. 檢測設備 ====================
+device = "cuda" if torch.cuda.is_available() else "cpu"
+print(f"\n🖥️ 使用設備: {device}")
+if device == "cpu":
+    print("⚠️ 警告: 使用 CPU 訓練會非常慢!")
+else:
+    print(f"✅ GPU 可用: {torch.cuda.get_device_name(0)}")
+
+if class_weights is not None and device == "cuda":
+    class_weights = class_weights.to(device)
+
+# ==================== 6. 載入模型和 Tokenizer ====================
+print("\n🤖 載入 Llama 模型和 Tokenizer...")
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+if tokenizer.pad_token is None:
+    tokenizer.pad_token = tokenizer.eos_token
+    tokenizer.pad_token_id = tokenizer.eos_token_id
+
+# ==================== 7. 載入未微調的基礎模型 (用於比較) ====================
+print("\n📦 載入未微調的基礎模型 (Baseline)...")
+baseline_model = AutoModelForSequenceClassification.from_pretrained(
+    MODEL_NAME,
+    num_labels=2,
+    torch_dtype=torch.float16 if device == "cuda" else torch.float32,
+    device_map="auto" if device == "cuda" else None
+)
+baseline_model.config.pad_token_id = tokenizer.pad_token_id
+print("✅ Baseline 模型載入完成")
+
+# ==================== 8. 載入要微調的模型 ====================
+print("\n🔧 載入用於微調的模型...")
+base_model = AutoModelForSequenceClassification.from_pretrained(
+    MODEL_NAME,
+    num_labels=2,
+    torch_dtype=torch.float16 if device == "cuda" else torch.float32,
+    device_map="auto" if device == "cuda" else None
+)
+base_model.config.pad_token_id = tokenizer.pad_token_id
+print("✅ 基礎模型載入完成")
+
+# ==================== 9. 配置 LoRA ====================
+print("\n🔧 配置 LoRA...")
+lora_config = LoraConfig(
+    task_type=TaskType.SEQ_CLS,
+    r=TRAIN_CONFIG["lora_r"],
+    lora_alpha=TRAIN_CONFIG["lora_alpha"],
+    lora_dropout=0.1,
+    target_modules=["q_proj", "v_proj"],
+    bias="none"
+)
+
+model = get_peft_model(base_model, lora_config)
+trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
+total_params = sum(p.numel() for p in model.parameters())
+print(f"✅ LoRA 配置完成")
+print(f"   可訓練參數: {trainable_params:,} ({trainable_params/total_params*100:.2f}%)")
+
+# ==================== 10. 預處理數據 ====================
+print("\n🔄 預處理數據...")
+
+def preprocess_function(examples):
+    return tokenizer(
+        examples['Text'],
+        truncation=True,
+        padding='max_length',
+        max_length=MAX_LENGTH
+    )
+
+tokenized_dataset = dataset.map(preprocess_function, batched=True, remove_columns=['Text'])
+tokenized_dataset = tokenized_dataset.rename_column("nbcd", "labels")
+print("✅ 數據預處理完成")
+
+# ==================== 11. 評估指標函數 ====================
+def compute_metrics(eval_pred):
+    predictions, labels = eval_pred
+    predictions = np.argmax(predictions, axis=1)
+    
+    accuracy = accuracy_score(labels, predictions)
+    precision, recall, f1, _ = precision_recall_fscore_support(
+        labels, predictions, average='binary', zero_division=0
+    )
+    
+    return {
+        'accuracy': accuracy,
+        'precision': precision,
+        'recall': recall,
+        'f1': f1
+    }
+
+# ==================== 12. 評估 Baseline 模型 (未微調) ====================
+print("\n" + "="*70)
+print("📊 評估未微調的 Baseline 模型...")
+print("="*70)
+
+baseline_trainer = Trainer(
+    model=baseline_model,
+    args=TrainingArguments(
+        output_dir="./temp_baseline",
+        per_device_eval_batch_size=TRAIN_CONFIG["batch_size"],
+        bf16=(device == "cuda"),
+        report_to="none"
+    ),
+    tokenizer=tokenizer,
+    data_collator=DataCollatorWithPadding(tokenizer=tokenizer),
+    compute_metrics=compute_metrics
+)
+
+baseline_train_results = baseline_trainer.evaluate(eval_dataset=tokenized_dataset['train'])
+baseline_test_results = baseline_trainer.evaluate(eval_dataset=tokenized_dataset['test'])
+
+print("\n🔍 Baseline 模型 - 訓練集結果:")
+print(f"  Accuracy:  {baseline_train_results['eval_accuracy']:.4f}")
+print(f"  Precision: {baseline_train_results['eval_precision']:.4f}")
+print(f"  Recall:    {baseline_train_results['eval_recall']:.4f}")
+print(f"  F1 Score:  {baseline_train_results['eval_f1']:.4f}")
+
+print("\n🔍 Baseline 模型 - 測試集結果:")
+print(f"  Accuracy:  {baseline_test_results['eval_accuracy']:.4f}")
+print(f"  Precision: {baseline_test_results['eval_precision']:.4f}")
+print(f"  Recall:    {baseline_test_results['eval_recall']:.4f}")
+print(f"  F1 Score:  {baseline_test_results['eval_f1']:.4f}")
+
+# ==================== 13. 自定義 Trainer ====================
+if BALANCE_CONFIG['use_class_weights']:
+    class WeightedTrainer(Trainer):
+        def __init__(self, *args, class_weights=None, **kwargs):
+            super().__init__(*args, **kwargs)
+            self.class_weights = class_weights
+        
+        def compute_loss(self, model, inputs, return_outputs=False, **kwargs):
+            labels = inputs.pop("labels")
+            outputs = model(**inputs)
+            logits = outputs.logits
+            
+            loss_fct = torch.nn.CrossEntropyLoss(weight=self.class_weights)
+            loss = loss_fct(logits.view(-1, self.model.config.num_labels), labels.view(-1))
+            
+            return (loss, outputs) if return_outputs else loss
+    
+    TrainerClass = WeightedTrainer
+else:
+    TrainerClass = Trainer
+
+# ==================== 14. 訓練配置 ====================
+print("\n" + "="*70)
+print("⚙️ 配置微調訓練器...")
+print("="*70)
+
+training_args = TrainingArguments(
+    output_dir=OUTPUT_DIR,
+    num_train_epochs=TRAIN_CONFIG["num_epochs"],
+    per_device_train_batch_size=TRAIN_CONFIG["batch_size"],
+    per_device_eval_batch_size=TRAIN_CONFIG["batch_size"],
+    learning_rate=TRAIN_CONFIG["learning_rate"],
+    weight_decay=0.01,
+    eval_strategy="epoch",
+    save_strategy="epoch",
+    load_best_model_at_end=True,
+    metric_for_best_model="f1",
+    logging_dir=f"{OUTPUT_DIR}/logs",
+    logging_steps=10,
+    bf16=(device == "cuda"),
+    gradient_accumulation_steps=2,
+    warmup_steps=50,
+    report_to="none",
+    seed=42
+)
+
+if BALANCE_CONFIG['use_class_weights']:
+    trainer = TrainerClass(
+        model=model,
+        args=training_args,
+        train_dataset=tokenized_dataset['train'],
+        eval_dataset=tokenized_dataset['test'],
+        tokenizer=tokenizer,
+        data_collator=DataCollatorWithPadding(tokenizer=tokenizer),
+        compute_metrics=compute_metrics,
+        class_weights=class_weights
+    )
+else:
+    trainer = TrainerClass(
+        model=model,
+        args=training_args,
+        train_dataset=tokenized_dataset['train'],
+        eval_dataset=tokenized_dataset['test'],
+        tokenizer=tokenizer,
+        data_collator=DataCollatorWithPadding(tokenizer=tokenizer),
+        compute_metrics=compute_metrics
+    )
+
+# ==================== 15. 開始訓練 ====================
+print("\n" + "="*70)
+print("🚀 開始微調訓練...")
+print("="*70 + "\n")
+
+start_time = datetime.now()
+
+try:
+    train_result = trainer.train()
+    end_time = datetime.now()
+    duration = (end_time - start_time).total_seconds() / 60
+    
+    print("\n" + "="*70)
+    print(f"✅ 訓練完成!")
+    print(f"   耗時: {duration:.1f} 分鐘")
+    print("="*70)
+    
+except Exception as e:
+    print(f"\n❌ 訓練失敗: {e}")
+    import traceback
+    traceback.print_exc()
+    exit(1)
+
+# ==================== 16. 評估微調後的模型 ====================
+print("\n" + "="*70)
+print("📊 評估微調後的模型...")
+print("="*70)
+
+finetuned_train_results = trainer.evaluate(eval_dataset=tokenized_dataset['train'])
+finetuned_test_results = trainer.evaluate(eval_dataset=tokenized_dataset['test'])
+
+print("\n🔍 微調模型 - 訓練集結果:")
+print(f"  Accuracy:  {finetuned_train_results['eval_accuracy']:.4f}")
+print(f"  Precision: {finetuned_train_results['eval_precision']:.4f}")
+print(f"  Recall:    {finetuned_train_results['eval_recall']:.4f}")
+print(f"  F1 Score:  {finetuned_train_results['eval_f1']:.4f}")
+
+print("\n🔍 微調模型 - 測試集結果:")
+print(f"  Accuracy:  {finetuned_test_results['eval_accuracy']:.4f}")
+print(f"  Precision: {finetuned_test_results['eval_precision']:.4f}")
+print(f"  Recall:    {finetuned_test_results['eval_recall']:.4f}")
+print(f"  F1 Score:  {finetuned_test_results['eval_f1']:.4f}")
+
+# ==================== 17. 比較結果 ====================
+print("\n" + "="*70)
+print("📈 Baseline vs Fine-tuned 比較 (測試集)")
+print("="*70)
+
+metrics = ['accuracy', 'precision', 'recall', 'f1']
+print(f"\n{'指標':<12} {'Baseline':<12} {'Fine-tuned':<12} {'改善':<12} {'狀態'}")
+print("-" * 70)
+
+for metric in metrics:
+    baseline_val = baseline_test_results[f'eval_{metric}']
+    finetuned_val = finetuned_test_results[f'eval_{metric}']
+    improvement = finetuned_val - baseline_val
+    improvement_pct = (improvement / baseline_val * 100) if baseline_val > 0 else 0
+    
+    status = "✅ 提升" if improvement > 0 else "⚠️ 下降" if improvement < 0 else "➖ 持平"
+    
+    print(f"{metric.capitalize():<12} {baseline_val:<12.4f} {finetuned_val:<12.4f} "
+          f"{improvement:+.4f} ({improvement_pct:+.1f}%)  {status}")
+
+print("="*70)
+
+# ==================== 18. 測試推論比較 ====================
+print("\n" + "="*70)
+print("🧪 測試推論比較 (5個樣本)")
+print("="*70)
+
+def predict_with_model(model_obj, text):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=MAX_LENGTH)
+    if device == "cuda":
+        inputs = {k: v.to(model_obj.device) for k, v in inputs.items()}
+    
+    with torch.no_grad():
+        outputs = model_obj(**inputs)
+        probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
+        predicted_class = torch.argmax(probs, dim=-1).item()
+        confidence = probs[0][predicted_class].item()
+    
+    return predicted_class, confidence
+
+test_samples = test_df.head(5)
+
+baseline_correct = 0
+finetuned_correct = 0
+baseline_class1_correct = 0
+finetuned_class1_correct = 0
+class1_total = 0
+
+for idx, (_, row) in enumerate(test_samples.iterrows(), 1):
+    true_label = row['nbcd']
+    text = row['Text']
+    
+    # Baseline 預測
+    baseline_pred, baseline_conf = predict_with_model(baseline_model, text)
+    baseline_match = "✅" if baseline_pred == true_label else "❌"
+    if baseline_pred == true_label:
+        baseline_correct += 1
+    
+    # Fine-tuned 預測
+    finetuned_pred, finetuned_conf = predict_with_model(model, text)
+    finetuned_match = "✅" if finetuned_pred == true_label else "❌"
+    if finetuned_pred == true_label:
+        finetuned_correct += 1
+    
+    # Class 1 統計
+    if true_label == 1:
+        class1_total += 1
+        if baseline_pred == 1:
+            baseline_class1_correct += 1
+        if finetuned_pred == 1:
+            finetuned_class1_correct += 1
+    
+    print(f"\n樣本 {idx} (實際標籤: {true_label}):")
+    print(f"  文本: {text[:100]}...")
+    print(f"  {baseline_match} Baseline:    預測={baseline_pred}  信心度={baseline_conf:.3f}")
+    print(f"  {finetuned_match} Fine-tuned:  預測={finetuned_pred}  信心度={finetuned_conf:.3f}")
+
+print("\n" + "="*70)
+print("📊 5個樣本預測準確率:")
+print(f"  Baseline:    {baseline_correct}/5 = {baseline_correct/5*100:.1f}%")
+print(f"  Fine-tuned:  {finetuned_correct}/5 = {finetuned_correct/5*100:.1f}%")
+if class1_total > 0:
+    print(f"\n  Class 1 識別率 (共 {class1_total} 個):")
+    print(f"    Baseline:    {baseline_class1_correct}/{class1_total}")
+    print(f"    Fine-tuned:  {finetuned_class1_correct}/{class1_total}")
+print("="*70)
+
+# ==================== 19. 保存模型和結果 ====================
+print("\n💾 保存模型和結果...")
+trainer.save_model()
+tokenizer.save_pretrained(OUTPUT_DIR)
+
+comparison_results = {
+    "model": MODEL_NAME,
+    "config": TRAIN_CONFIG,
+    "balance_config": BALANCE_CONFIG,
+    "train_time_minutes": duration,
+    "baseline_results": {
+        "train": {
+            "accuracy": float(baseline_train_results['eval_accuracy']),
+            "precision": float(baseline_train_results['eval_precision']),
+            "recall": float(baseline_train_results['eval_recall']),
+            "f1": float(baseline_train_results['eval_f1'])
+        },
+        "test": {
+            "accuracy": float(baseline_test_results['eval_accuracy']),
+            "precision": float(baseline_test_results['eval_precision']),
+            "recall": float(baseline_test_results['eval_recall']),
+            "f1": float(baseline_test_results['eval_f1'])
+        }
+    },
+    "finetuned_results": {
+        "train": {
+            "accuracy": float(finetuned_train_results['eval_accuracy']),
+            "precision": float(finetuned_train_results['eval_precision']),
+            "recall": float(finetuned_train_results['eval_recall']),
+            "f1": float(finetuned_train_results['eval_f1'])
+        },
+        "test": {
+            "accuracy": float(finetuned_test_results['eval_accuracy']),
+            "precision": float(finetuned_test_results['eval_precision']),
+            "recall": float(finetuned_test_results['eval_recall']),
+            "f1": float(finetuned_test_results['eval_f1'])
+        }
+    },
+    "improvements": {
+        "accuracy": float(finetuned_test_results['eval_accuracy'] - baseline_test_results['eval_accuracy']),
+        "precision": float(finetuned_test_results['eval_precision'] - baseline_test_results['eval_precision']),
+        "recall": float(finetuned_test_results['eval_recall'] - baseline_test_results['eval_recall']),
+        "f1": float(finetuned_test_results['eval_f1'] - baseline_test_results['eval_f1'])
+    },
+    "timestamp": datetime.now().isoformat(),
+    "device": device
+}
+
+with open(f"{OUTPUT_DIR}/comparison_results.json", "w", encoding='utf-8') as f:
+    json.dump(comparison_results, f, indent=2, ensure_ascii=False)
+
+print(f"✅ 結果已保存到: {OUTPUT_DIR}/comparison_results.json")
+
+# ==================== 20. 總結 ====================
+print("\n" + "="*70)
+print("🎉 訓練和比較流程全部完成!")
+print("="*70)
+print(f"\n📦 輸出內容:")
+print(f"  微調模型: {OUTPUT_DIR}/")
+print(f"  比較結果: {OUTPUT_DIR}/comparison_results.json")
+print(f"  訓練日誌: {OUTPUT_DIR}/logs/")
+print("\n💡 關鍵發現:")
+print(f"  測試集 F1 Score 提升: {comparison_results['improvements']['f1']:+.4f}")
+print(f"  測試集 Recall 提升: {comparison_results['improvements']['recall']:+.4f}")
+print(f"  測試集 Accuracy 提升: {comparison_results['improvements']['accuracy']:+.4f}")
+print("="*70 + "\n")