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Dockerfile

@@ -11,5 +11,4 @@ COPY . .
 
 EXPOSE 5001
 
-# 确保模块名和 Flask 实例名正确（默认是 app:app）
+# 确保模块名和 Flask 实例名正确（默认是 app]
-CMD ["uvicorn", "app:app", "--reload", "--host", "0.0.0.0", "--port", "5001"]

a.py

@@ -0,0 +1,208 @@
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from modelscope import AutoTokenizer, AutoModelForSequenceClassification
+from transformers import (
+    Trainer,
+    TrainingArguments,
+    EarlyStoppingCallback
+)
+import torch
+from torch.utils.data import Dataset
+from tqdm import tqdm
+import warnings
+import re  # 用于正则表达式清洗
+from sklearn.preprocessing import LabelEncoder
+import joblib
+
+# 1. 参数配置
+class Config:
+    MODEL_NAME = "bert-base-chinese"
+    MAX_LENGTH = 64
+    BATCH_SIZE = 32
+    NUM_EPOCHS = 5
+    LEARNING_RATE = 2e-5
+    WARMUP_STEPS = 500
+    WEIGHT_DECAY = 0.01
+    FP16 = torch.cuda.is_available()
+    OUTPUT_DIR = "./results/single_level"
+    LOG_DIR = "./logs"
+    SAVE_DIR = "./saved_model/single_level"
+    DEVICE = "cuda" if FP16 else "cpu"
+
+
+# 2. 数据加载与预处理
+def load_data(file_path):
+    try:
+        df = pd.read_csv(file_path)
+        assert {'sentence', 'label'}.issubset(df.columns), "数据必须包含'sentence'和'label'列"
+        print(f"✅ 数据加载成功 | 样本量: {len(df)} | 分类数: {df['label'].nunique()}")
+        return df
+    except Exception as e:
+        warnings.warn(f"❌ 数据加载失败: {str(e)}")
+        raise
+
+
+# 新增：数据清洗函数 - 只保留中文字符
+def clean_chinese_text(text):
+    """
+    清洗文本，保留中文字符、英文字母和数字，去除空格和特殊符号
+    """
+    if not isinstance(text, str):
+        return ""
+    # 保留中文（\u4e00-\u9fa5）、英文（a-zA-Z）和数字（0-9），去除其他所有字符（包括空格、标点等）
+    cleaned_text = re.sub(r'[^\u4e00-\u9fa5a-zA-Z0-9]', '', text)
+    return cleaned_text.strip()
+
+# 3. Dataset
+class TextDataset(Dataset):
+    def __init__(self, dataframe, tokenizer, text_col="sentence", label_col="label"):
+        self.data = dataframe
+        self.tokenizer = tokenizer
+        self.text_col = text_col
+        self.label_col = label_col
+
+        # 预计算编码（空间换时间）
+        self.encodings = tokenizer(
+            dataframe[text_col].tolist(),
+            max_length=Config.MAX_LENGTH,
+            padding="max_length",
+            truncation=True,
+            return_tensors="pt"
+        )
+        self.labels = torch.tensor(dataframe[label_col].values, dtype=torch.long)
+
+    def __len__(self):
+        return len(self.data)
+
+    def __getitem__(self, idx):
+        return {
+            "input_ids": self.encodings["input_ids"][idx],
+            "attention_mask": self.encodings["attention_mask"][idx],
+            "labels": self.labels[idx]
+        }
+
+
+# 4. 模型初始化
+def init_model(num_labels):
+    tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-chinese")
+    model = AutoModelForSequenceClassification.from_pretrained("google-bert/bert-base-chinese",num_labels=num_labels)
+    return tokenizer, model
+
+
+# 5. 训练配置
+def get_training_args():
+    return TrainingArguments(
+        output_dir=Config.OUTPUT_DIR, #输出目录
+        num_train_epochs=Config.NUM_EPOCHS,  #训练轮数，适度训练会增加精度，训练过多可能会因为训练数据中的噪声（错误数据）导致精度下降，解决方案：正则，早停，数据增强；梯度爆炸
+        per_device_train_batch_size=Config.BATCH_SIZE, #前向传播（forward pass）处理的样本数，比如：若 per_device_train_batch_size=32，且使用 2 块 GPU，则每块 GPU 会独立处理 32 个样本。总批量大小（total_batch_size）由以下公式决定：total_batch_size=per_device_train_batch_size×GPU 数量×gradient_accumulation_steps
+        per_device_eval_batch_size=Config.BATCH_SIZE * 2,
+        learning_rate=Config.LEARNING_RATE,
+        warmup_steps=Config.WARMUP_STEPS,
+        weight_decay=Config.WEIGHT_DECAY,
+        logging_dir=Config.LOG_DIR,
+        logging_steps=10,
+        eval_strategy="steps",
+        eval_steps=100,
+        save_strategy="steps",
+        save_steps=200,
+        load_best_model_at_end=True,
+        metric_for_best_model="eval_loss",
+        greater_is_better=False,
+        fp16=Config.FP16,
+        gradient_accumulation_steps=2,
+        report_to="none",  # 禁用wandb等报告
+        seed=42
+    )
+
+
+# 6.推理完测试函数
+@torch.no_grad()
+def batch_predict(texts, model, tokenizer, label_map, top_k=1, batch_size=16):
+    model.eval()
+    all_results = []
+
+    for i in tqdm(range(0, len(texts), batch_size), desc="预测中"):
+        batch = texts[i:i + batch_size]
+        inputs = tokenizer(
+            batch,
+            return_tensors="pt",
+            truncation=True,
+            padding=True,
+            max_length=Config.MAX_LENGTH
+        ).to(Config.DEVICE)
+
+        outputs = model(**inputs)
+        probs = torch.softmax(outputs.logits, dim=1).cpu()
+
+        for prob in probs:
+            top_probs, top_indices = torch.topk(prob, k=top_k)
+            all_results.extend([
+                {
+                    "category": label_map[idx.item()],
+                    "confidence": prob.item()
+                }
+                for prob, idx in zip(top_probs, top_indices)
+            ])
+
+    return all_results[:len(texts)]  # 处理非整除情况
+
+
+# 主流程
+if __name__ == "__main__":
+    # 1. 加载数据
+    df = load_data("order_address.csv")
+
+    # 2. 数据清洗 - 只保留中文
+    print("🧼 开始清洗文本数据...")
+    df['sentence'] = df['sentence'].apply(clean_chinese_text)
+    df = df[df['sentence'].str.len() > 0].reset_index(drop=True)
+    print(f"✅ 数据清洗完成 | 剩余样本量: {len(df)}")
+
+    # 3. 处理中文标签：映射为数值ID，并保存映射关系
+    print("🏷️ 处理中文标签...")
+    label_encoder = LabelEncoder()
+    df['label_id'] = label_encoder.fit_transform(df['label'])  # 中文标签 → 数值ID
+    label_map = {i: label for i, label in enumerate(label_encoder.classes_)}  # 数值ID → 中文标签
+    print(f"标签映射示例: {label_map}")
+
+    # 保存标签映射器（供推理时使用）
+    joblib.dump(label_encoder, "label_encoder.pkl")
+    print(f"✅ 标签映射完成 | 类别数: {len(label_map)}")
+
+    # 4. 划分数据集
+    train_df, test_df = train_test_split(
+        df, test_size=0.2, random_state=42, stratify=df["label_id"]
+    )
+
+    # 5. 初始化模型
+    num_labels = len(label_map)
+    tokenizer, model = init_model(num_labels)
+
+    # 6. 准备数据集（使用 label_id 列）
+    train_dataset = TextDataset(train_df, tokenizer, label_col="label_id")
+    test_dataset = TextDataset(test_df, tokenizer, label_col="label_id")
+
+    # 7. 训练配置
+    training_args = get_training_args()
+
+    # 8. 训练器
+    trainer = Trainer(
+        model=model,
+        args=training_args,
+        train_dataset=train_dataset,
+        eval_dataset=test_dataset,
+        callbacks=[EarlyStoppingCallback(early_stopping_patience=3)]
+    )
+
+    # 9. 训练和保存
+    trainer.train()
+    model.save_pretrained(Config.SAVE_DIR)
+    tokenizer.save_pretrained(Config.SAVE_DIR)
+    # 12. 测试推理
+    test_samples = ["山东省济南市莱芜区碧桂园天樾422502", "广东省广州市花都区狮岭镇山前旅游大道18号机车检修段", "江苏省苏州市吴中区吴中区木渎镇枫瑞路85号诺德·长枫雅苑北区10栋-303"]
+    # 先清洗测试样本
+    cleaned_samples = [clean_chinese_text(s) for s in test_samples]
+    predictions = batch_predict(cleaned_samples, model, tokenizer, label_map)
+    for sample, pred in zip(test_samples, predictions):
+        print(
+            f"输入: {sample}\n清洗后: {clean_chinese_text(sample)}\n预测: {pred['category']} (置信度: {pred['confidence']:.2f})\n")

app.py

@@ -1,8 +1,6 @@
 from fastapi import FastAPI
 from pydantic import BaseModel
 from modelscope import AutoModelForCausalLM, AutoTokenizer
-import re
-from typing import List
 
 app = FastAPI()
 
@@ -29,8 +27,8 @@ async def extract_brand(request: BrandExtractionRequest):
     # 构建prompt
     prompt = (
         f"商品名称：{goods}\n"
-        "-只需要返回一个品牌的名字，去掉多余的描述\n"
+        "-只需要返回品牌名字，去掉多余的描述\n"
-        f"-请在以下品牌中选择：{brand_set}"
+        #f"-请在以下品牌中选择：{brand_set}"
     )
 
     messages = [
@@ -64,6 +62,7 @@ async def extract_brand(request: BrandExtractionRequest):
 
     # 后处理：确保返回的品牌在brand_set中，否则返回"失败"
     extracted_brand = content.strip()
+    print(extracted_brand)
     if extracted_brand not in brand_set:
         # 尝试在goods中直接查找品牌名（简单匹配）
         for brand in brand_set: