model_classifier.git

			@@ -30,7 +30,7 @@
			# 交叉验证配置
			N_SPLITS = 3 # CPU环境下减少折数
			N_SEEDS = 1 # CPU环境下减少种子数量


			# 确保标签列表完整
			LABELS = [
			"O",
			@@ -72,40 +72,40 @@
			LEARNING_RATE = 3e-5
			WARMUP_RATIO = 0.1
			WEIGHT_DECAY = 0.01


			# 数据增强配置
			USE_DATA_AUGMENTATION = False
			AUGMENTATION_RATIO = 0.3


			# 训练策略
			GRADIENT_ACCUMULATION_STEPS = 4
			EVAL_STEPS = 25
			LOGGING_STEPS = 10
			SAVE_STEPS = 25 # 添加保存步数
			SAVE_TOTAL_LIMIT = 2 # 添加保存检查点数量限制


			# 路径配置
			DATA_PATH = "data/repayment.txt"
			MODEL_PATH = "./models/repayment_model"
			LOG_PATH = "./logs_repayment"


			# 训练配置优化
			SEED = 42
			TEST_SIZE = 0.1
			EARLY_STOPPING_PATIENCE = 2


			# CPU环境配置
			MAX_GRAD_NORM = 1.0
			FP16 = False # CPU环境下关闭FP16


			# CPU环境下的数据加载优化
			DATALOADER_NUM_WORKERS = 0 # CPU环境下设为0
			DATALOADER_PIN_MEMORY = False # CPU环境下关闭


			# 交叉验证配置
			N_SPLITS = 3
			N_SEEDS = 1


			# 标签列表
			LABELS = [
			"O",
			@@ -149,40 +149,40 @@
			LEARNING_RATE = 3e-5
			WARMUP_RATIO = 0.1
			WEIGHT_DECAY = 0.01


			# 数据增强配置
			USE_DATA_AUGMENTATION = False
			AUGMENTATION_RATIO = 0.3


			# 训练策略
			GRADIENT_ACCUMULATION_STEPS = 4
			EVAL_STEPS = 25
			LOGGING_STEPS = 10
			SAVE_STEPS = 25
			SAVE_TOTAL_LIMIT = 2


			# 路径配置
			DATA_PATH = "data/income.txt"
			MODEL_PATH = "./models/income_model"
			LOG_PATH = "./logs_income"


			# 训练配置优化
			SEED = 42
			TEST_SIZE = 0.1
			EARLY_STOPPING_PATIENCE = 2


			# CPU环境配置
			MAX_GRAD_NORM = 1.0
			FP16 = False


			# CPU环境下的数据加载优化
			DATALOADER_NUM_WORKERS = 0
			DATALOADER_PIN_MEMORY = False


			# 交叉验证配置
			N_SPLITS = 3
			N_SEEDS = 1


			# 标签列表
			LABELS = [
			"O",
			@@ -211,4 +211,154 @@
			'max_integer_digits': 12, # 整数部分最大位数
			'currency_symbols': ['￥', '¥', 'RMB', '元'], # 货币符号
			'decimal_context_range': 3 # 查找小数点的上下文范围
			}

			class FlightNERConfig:
			# 优化模型参数 (与 RepaymentNERConfig 保持一致)
			MODEL_NAME = "bert-base-chinese"
			MAX_LENGTH = 128
			BATCH_SIZE = 4
			EPOCHS = 10
			LEARNING_RATE = 3e-5
			WARMUP_RATIO = 0.1
			WEIGHT_DECAY = 0.01

			# 训练策略
			GRADIENT_ACCUMULATION_STEPS = 4
			EVAL_STEPS = 25
			LOGGING_STEPS = 10
			SAVE_STEPS = 25
			SAVE_TOTAL_LIMIT = 2

			# 路径配置
			DATA_PATH = "data/flight.txt"
			MODEL_PATH = "./models/flight_model"
			LOG_PATH = "./logs_flight"

			# 训练配置
			SEED = 42
			TEST_SIZE = 0.1
			EARLY_STOPPING_PATIENCE = 2

			# CPU环境配置
			MAX_GRAD_NORM = 1.0
			FP16 = False
			DATALOADER_NUM_WORKERS = 0
			DATALOADER_PIN_MEMORY = False

			# 交叉验证配置
			N_SPLITS = 3
			N_SEEDS = 3 # 增加种子数量以提高模型稳定性

			# 标签列表 - 保持与需求一致
			LABELS = [
			"O",
			"B-FLIGHT", "I-FLIGHT", # 航班号
			"B-COMPANY", "I-COMPANY", # 航空公司
			"B-START", "I-START", # 出发地
			"B-END", "I-END", # 目的地
			"B-DATE", "I-DATE", # 日期
			"B-TIME", "I-TIME", # 时间
			"B-DEPARTURE_TIME", "I-DEPARTURE_TIME", # 起飞时间
			"B-ARRIVAL_TIME", "I-ARRIVAL_TIME", # 到达时间
			"B-TICKET_NUM", "I-TICKET_NUM", # 机票号码
			"B-SEAT", "I-SEAT" # 座位等信息
			]

			# 实体长度限制 - 更新键名与LABELS一致
			MAX_ENTITY_LENGTH = {
			"FLIGHT": 10, # 航班号
			"COMPANY": 15, # 航空公司
			"START": 10, # 出发地
			"END": 10, # 目的地
			"DATE": 15, # 日期
			"TIME": 10, # 时间
			"DEPARTURE_TIME": 10, # 起飞时间
			"ARRIVAL_TIME": 10, # 到达时间
			"TICKET_NUM": 10, # 用户姓名
			"SEAT": 10 # 座位等信息
			}

			# 航班号配置
			FLIGHT_CONFIG = {
			'pattern': r'[A-Z]{2}\d{3,4}',
			'min_length': 4,
			'max_length': 7,
			'carrier_codes': ['CA', 'MU', 'CZ', 'HU', '3U', 'ZH', 'FM', 'MF', 'SC', '9C'] # 常见航司代码
			}

			class TrainNERConfig:
			# 模型参数
			MODEL_NAME = "bert-base-chinese"
			MAX_LENGTH = 128
			BATCH_SIZE = 4
			EPOCHS = 10
			LEARNING_RATE = 3e-5
			WARMUP_RATIO = 0.1
			WEIGHT_DECAY = 0.01

			# 训练策略
			GRADIENT_ACCUMULATION_STEPS = 4
			EVAL_STEPS = 25
			LOGGING_STEPS = 10
			SAVE_STEPS = 25
			SAVE_TOTAL_LIMIT = 2

			# 路径配置
			DATA_PATH = "data/train.txt"
			MODEL_PATH = "./models/train_model"
			LOG_PATH = "./logs_train"

			# 训练配置
			SEED = 42
			TEST_SIZE = 0.1
			EARLY_STOPPING_PATIENCE = 2

			# CPU环境配置
			MAX_GRAD_NORM = 1.0
			FP16 = False
			DATALOADER_NUM_WORKERS = 0
			DATALOADER_PIN_MEMORY = False

			# 交叉验证配置
			N_SPLITS = 3
			N_SEEDS = 3 # 增加种子数量以提高模型稳定性

			# 标签列表
			LABELS = [
			"O",
			"B-COMPANY", "I-COMPANY", # 车次
			"B-TRIPS", "I-TRIPS", # 车次
			"B-START", "I-START", # 出发站
			"B-END", "I-END", # 到达站
			"B-DATE", "I-DATE", # 日期
			"B-TIME", "I-TIME", # 时间
			"B-SEAT", "I-SEAT", # 座位等信息
			"B-NAME", "I-NAME" # 用户姓名
			]

			# 实体长度限制 - 更新键名与LABELS一致
			MAX_ENTITY_LENGTH = {
			"COMPANY": 8, # 12306
			"TRIPS": 8, # 车次
			"START": 10, # 出发站
			"END": 10, # 到达站
			"DATE": 15, # 日期
			"TIME": 10, # 时间
			"SEAT": 10, # 座位等信息
			"NAME": 10 # 用户姓名
			}

			# 车次配置
			TRIPS_CONFIG = {
			'patterns': [
			r'[GDCZTKY]\d{1,2}', # G1, D1, C1等
			r'[GDCZTKY]\d{1,2}/\d{1,2}', # G1/2等联运车次
			r'[GDCZTKY]\d{1,2}-\d{1,2}', # G1-2等联运车次
			r'\d{1,4}', # 普通车次如1234次
			r'[A-Z]\d{1,4}' # Z1234等特殊车次
			],
			'min_length': 1,
			'max_length': 8,
			'train_types': ['G', 'D', 'C', 'Z', 'T', 'K', 'Y'] # 车次类型前缀
			}