This commit is contained in:
renzhiyuan 2025-08-08 14:36:09 +08:00
parent 9e2e2e02a5
commit 49e6e2d29e
10 changed files with 407 additions and 7 deletions

8
.idea/.gitignore vendored Normal file
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# Default ignored files
/shelf/
/workspace.xml
# Editor-based HTTP Client requests
/httpRequests/
# Datasource local storage ignored files
/dataSources/
/dataSources.local.xml

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<component name="InspectionProjectProfileManager">
<profile version="1.0">
<option name="myName" value="Project Default" />
<inspection_tool class="PyPackageRequirementsInspection" enabled="true" level="WARNING" enabled_by_default="true">
<option name="ignoredPackages">
<value>
<list size="1">
<item index="0" class="java.lang.String" itemvalue="betterproto" />
</list>
</value>
</option>
</inspection_tool>
</profile>
</component>

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<component name="InspectionProjectProfileManager">
<settings>
<option name="USE_PROJECT_PROFILE" value="false" />
<version value="1.0" />
</settings>
</component>

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.idea/misc.xml Normal file
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<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="Black">
<option name="sdkName" value="/root/miniconda3" />
</component>
<component name="ProjectRootManager" version="2" project-jdk-name="311" project-jdk-type="Python SDK" />
</project>

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.idea/modules.xml Normal file
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<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="ProjectModuleManager">
<modules>
<module fileurl="file://$PROJECT_DIR$/.idea/select.iml" filepath="$PROJECT_DIR$/.idea/select.iml" />
</modules>
</component>
</project>

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.idea/select.iml Normal file
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<?xml version="1.0" encoding="UTF-8"?>
<module type="PYTHON_MODULE" version="4">
<component name="NewModuleRootManager">
<content url="file://$MODULE_DIR$" />
<orderEntry type="jdk" jdkName="311" jdkType="Python SDK" />
<orderEntry type="sourceFolder" forTests="false" />
</component>
</module>

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.idea/vcs.xml Normal file
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<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="VcsDirectoryMappings">
<mapping directory="$PROJECT_DIR$" vcs="Git" />
</component>
</project>

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bert_train.py Normal file
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import pandas as pd
from sklearn.model_selection import train_test_split
from transformers import (
BertTokenizer,
BertForSequenceClassification,
Trainer,
TrainingArguments,
EarlyStoppingCallback
)
import torch
from torch.utils.data import Dataset
from tqdm import tqdm
import os
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(集中管理)
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(添加异常处理和日志)
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''label'"
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):
"""
清洗文本只保留中文字符
"""
if not isinstance(text, str):
return ""
# 使用正则表达式匹配所有中文字符(包括中文标点符号)[^\u4e00-\u9fa5\u3000-\u303f\uff00-\uffef]
# 如果需要更严格的只保留汉字,可以使用:[\u4e00-\u9fa5]
cleaned_text = re.sub(r'[^\u4e00-\u9fa5]', '', text)
return cleaned_text.strip()
# 3. 优化Dataset添加内存缓存和批处理支持
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(添加设备移动)
def init_model(num_labels):
tokenizer = BertTokenizer.from_pretrained(Config.MODEL_NAME)
model = BertForSequenceClassification.from_pretrained(
Config.MODEL_NAME,
num_labels=num_labels,
ignore_mismatched_sizes=True # 可选
).to(Config.DEVICE)
return tokenizer, model
# 5. 训练配置(添加早停和梯度累积)
def get_training_args():
return TrainingArguments(
output_dir=Config.OUTPUT_DIR,
num_train_epochs=Config.NUM_EPOCHS,
per_device_train_batch_size=Config.BATCH_SIZE,
per_device_eval_batch_size=Config.BATCH_SIZE * 2, # 评估时可用更大batch
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, # 模拟更大batch
report_to="none", # 禁用wandb等报告
seed=42
)
# 6. 优化推理函数(添加批处理支持)
@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("goods_cate.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, "cate/label_encoder.pkl")
print(f"✅ 标签映射完成 | 类别数: {len(label_map)}")
# 4. 划分数据集(使用 label_id 列)
train_df, test_df = train_test_split(
df, test_size=0.2, random_state=42, stratify=df["label_id"] # 注意这里用 label_id
)
# 5. 初始化模型(使用数值标签的数量)
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") # 指定 label_col
test_dataset = TextDataset(test_df, tokenizer, label_col="label_id")
# 7. 训练配置(保持不变)
training_args = get_training_args()
# 8. 训练器(保持不变)
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(保持不变)
trainer.train()
model.save_pretrained(Config.SAVE_DIR)
tokenizer.save_pretrained(Config.SAVE_DIR)
# 12. 测试推理
test_samples = ["不二家棒棒糖", "iPhone 15", "无线鼠标"]
# 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")

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@ -10,7 +10,7 @@ logger = logging.getLogger(__name__)
try:
classifier = pipeline(
"zero-shot-classification",
model="./nlp_structbert_zero-shot-classification_chinese-large",
model="./nlp_structbert_zero-shot-classification_chinese-base",
device="cpu",
max_length=512,
ignore_mismatched_sizes=True
@ -20,10 +20,6 @@ except Exception as e:
logger.error(f"模型加载失败: {str(e)}")
raise
# 定义类别标签
level1 = [
"食品", "电器", "洗护", "女装", "手机","健康", "男装", "美妆", "电脑", "运动","内衣", "母婴", "数码", "百货", "鞋包","办公", "家装", "饰品", "车品", "图书","生鲜", "家纺", "宠物", "奢品", "其它", "药品"
]
# 创建Flask应用
app = Flask(__name__)
@ -49,12 +45,14 @@ def classify_text():
truncation=True
)
labels = result['labels']
print(result)
print(result['scores'][-1])
# 构建响应
response = {
"cate": labels[0],
"cate": result['labels'][-1],
"score": result['scores'][-1],
}
return jsonify(response)

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test_my_mode.py Normal file
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from flask import Flask, request, jsonify
import torch
from transformers import BertTokenizer, BertForSequenceClassification
import joblib
import re
from functools import lru_cache
from concurrent.futures import ThreadPoolExecutor
from typing import List, Dict
import threading
# 配置参数
MAX_LENGTH = 512
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
SAVE_DIR = "./cate1" # 模型路径
LABEL_ENCODER_PATH = "cate1/label_encoder.pkl" # 标签映射器路径
app = Flask(__name__)
# 全局变量锁
model_lock = threading.Lock()
class Predictor:
"""预测器类,用于管理模型和分词器的生命周期"""
_instance = None
def __new__(cls):
if cls._instance is None:
with model_lock:
if cls._instance is None: # 双重检查锁定
cls._instance = super().__new__(cls)
cls._instance._initialized = False
return cls._instance
def __init__(self):
if self._initialized:
return
# 加载模型和分词器
self.tokenizer = BertTokenizer.from_pretrained(SAVE_DIR)
self.model = BertForSequenceClassification.from_pretrained(SAVE_DIR).to(DEVICE)
self.model.eval()
# 加载标签映射器
self.label_encoder = joblib.load(LABEL_ENCODER_PATH)
self.label_map = {i: label for i, label in enumerate(self.label_encoder.classes_)}
# 创建线程池
self.executor = ThreadPoolExecutor(max_workers=4)
self._initialized = True
@lru_cache(maxsize=1000)
def clean_text(self, text: str) -> str:
"""文本清洗函数,带缓存"""
if not isinstance(text, str):
return ""
cleaned_text = re.sub(r'[^\u4e00-\u9fa5]', '', text)
return cleaned_text.strip()
def predict_single(self, text: str) -> Dict:
"""单个文本预测"""
with torch.no_grad():
cleaned_text = self.clean_text(text)
if not cleaned_text:
return {"category": "", "confidence": 0.0}
inputs = self.tokenizer(
cleaned_text,
return_tensors="pt",
truncation=True,
padding=True,
max_length=MAX_LENGTH
).to(DEVICE)
outputs = self.model(**inputs)
probs = torch.softmax(outputs.logits, dim=1).cpu()
top_prob, top_idx = torch.topk(probs, k=1)
#去掉置信度速度提升10%-30%
# top_idx = torch.argmax(outputs.logits, dim=1)
return {
"category": self.label_map[top_idx.item()],
"confidence": top_prob.item()
}
def batch_predict(self, texts: List[str]) -> List[Dict]:
"""批量预测(并发处理)"""
if not texts:
return []
# 使用线程池并发处理
futures = [self.executor.submit(self.predict_single, text) for text in texts]
return [future.result() for future in futures]
# 初始化预测器
predictor = Predictor()
@app.route('/predict', methods=['POST'])
def predict():
"""预测接口"""
data = request.get_json()
if not data or 'product_names' not in data:
return jsonify({"error": "Invalid request, 'product_names' is required"}), 400
product_names = data['product_names']
if not isinstance(product_names, list):
product_names = [product_names]
# 批量预测
results = predictor.batch_predict(product_names)
return jsonify({
"status": "success",
"predictions": results
})
@app.route('/health', methods=['GET'])
def health_check():
"""健康检查接口"""
return jsonify({"status": "healthy"})
if __name__ == '__main__':
# 生产环境建议使用:
# gunicorn -w 4 -b 0.0.0.0:5000 app:app --timeout 120
app.run(host='0.0.0.0', port=5002, threaded=True)