class NERConfig:
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# 优化模型参数
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MODEL_NAME = "bert-base-chinese"
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MAX_LENGTH = 128 # 获取更多上下文
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BATCH_SIZE = 4
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EPOCHS = 10 # 增加训练轮数
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LEARNING_RATE = 3e-5
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WARMUP_RATIO = 0.1
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WEIGHT_DECAY = 0.01
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# 数据增强配置
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USE_DATA_AUGMENTATION = False # 暂时关闭数据增强
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AUGMENTATION_RATIO = 0.3
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# 训练策略
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GRADIENT_ACCUMULATION_STEPS = 4
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EVAL_STEPS = 25 # 更频繁的验证
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LOGGING_STEPS = 10
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# 路径配置
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DATA_PATH = "data/sms_ner.txt"
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MODEL_PATH = "./models/ner_model"
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LOG_PATH = "./logs_ner"
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# 训练配置优化
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SEED = 42
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TEST_SIZE = 0.1 # 减少测试集比例，增加训练数据
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EARLY_STOPPING_PATIENCE = 2 # 增加早停耐心值
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# 交叉验证配置
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N_SPLITS = 3 # CPU环境下减少折数
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N_SEEDS = 1 # CPU环境下减少种子数量
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# 确保标签列表完整
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LABELS = [
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"O",
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"B-POST", "I-POST",
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"B-COMPANY", "I-COMPANY",
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"B-ADDRESS", "I-ADDRESS",
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"B-PICKUP_CODE", "I-PICKUP_CODE", # 添加取件码标签
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"B-STATION", "I-STATION" # 添加站点标签
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]
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# 增加训练数据质量控制
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MAX_ENTITY_LENGTH = {
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"PICKUP_CODE": 15, # 限制取件码最大长度
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"ADDRESS": 50,
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"COMPANY": 10,
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"POST": 10,
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"STATION": 20
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}
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# 取件码相关配置
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PICKUP_CODE_CONFIG = {
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'max_length': 15, # 取件码最大长度
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'invalid_words': ['的', '来', '到', '取', '件', '码'], # 取件码中不应该出现的词
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'valid_separators': ['-'], # 有效的分隔符
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'min_parts': 2, # 最少分段数
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'max_parts': 4, # 最多分段数
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'part_length': {
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'min': 1, # 每段最少字符数
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'max': 4 # 每段最多字符数
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}
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}
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class RepaymentNERConfig:
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# 优化模型参数
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MODEL_NAME = "bert-base-chinese"
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MAX_LENGTH = 128
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BATCH_SIZE = 4
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EPOCHS = 10
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LEARNING_RATE = 3e-5
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WARMUP_RATIO = 0.1
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WEIGHT_DECAY = 0.01
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# 数据增强配置
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USE_DATA_AUGMENTATION = False
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AUGMENTATION_RATIO = 0.3
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# 训练策略
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GRADIENT_ACCUMULATION_STEPS = 4
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EVAL_STEPS = 25
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LOGGING_STEPS = 10
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SAVE_STEPS = 25 # 添加保存步数
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SAVE_TOTAL_LIMIT = 2 # 添加保存检查点数量限制
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# 路径配置
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DATA_PATH = "data/repayment.txt"
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MODEL_PATH = "./models/repayment_model"
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LOG_PATH = "./logs_repayment"
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# 训练配置优化
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SEED = 42
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TEST_SIZE = 0.1
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EARLY_STOPPING_PATIENCE = 2
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# CPU环境配置
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MAX_GRAD_NORM = 1.0
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FP16 = False # CPU环境下关闭FP16
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# CPU环境下的数据加载优化
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DATALOADER_NUM_WORKERS = 0 # CPU环境下设为0
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DATALOADER_PIN_MEMORY = False # CPU环境下关闭
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# 交叉验证配置
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N_SPLITS = 3
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N_SEEDS = 1
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# 标签列表
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LABELS = [
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"O",
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"B-BANK", "I-BANK", # 还款主体
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"B-TYPE", "I-TYPE", # 还款类型(花呗/信用卡/房贷等)
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"B-PICKUP_CODE", "I-PICKUP_CODE", # 还款金额
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"B-DATE", "I-DATE", # 还款日期
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"B-NUMBER", "I-NUMBER", # 账号尾号
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"B-MIN_CODE", "I-MIN_CODE" # 最低还款金额
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]
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# 金额相关配置
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AMOUNT_CONFIG = {
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'max_length': 20, # 增加金额最大长度
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'invalid_words': ['共', '计', '额', '为', '是', '人民币', '￥', '¥', '元', '应还', '本期'],
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'valid_separators': ['.', ','], # 有效的分隔符
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'decimal_places': 2, # 小数位数
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'max_integer_digits': 12, # 增加整数部分最大位数
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'currency_symbols': ['￥', '¥', 'RMB', '元'], # 货币符号
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'amount_keywords': ['金额', '应还', '欠款', '待还', '本金', '本期'],
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'min_amount_keywords': ['最低', '最少', '至少'],
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'decimal_context_range': 3 # 查找小数点的上下文范围
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}
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# 修改实体长度限制
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MAX_ENTITY_LENGTH = {
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"BANK": 15, # 增加银行名称最大长度
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"TYPE": 10, # 增加还款类型最大长度
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"PICKUP_CODE": 20, # 金额最大长度
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"DATE": 15, # 增加日期最大长度
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"NUMBER": 10, # 增加账号尾号最大长度
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"MIN_CODE": 20 # 最低还款金额最大长度
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}
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class IncomeNERConfig:
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# 优化模型参数 (与 RepaymentNERConfig 保持一致)
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MODEL_NAME = "bert-base-chinese"
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MAX_LENGTH = 128
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BATCH_SIZE = 4
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EPOCHS = 10
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LEARNING_RATE = 3e-5
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WARMUP_RATIO = 0.1
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WEIGHT_DECAY = 0.01
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# 数据增强配置
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USE_DATA_AUGMENTATION = False
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AUGMENTATION_RATIO = 0.3
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# 训练策略
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GRADIENT_ACCUMULATION_STEPS = 4
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EVAL_STEPS = 25
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LOGGING_STEPS = 10
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SAVE_STEPS = 25
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SAVE_TOTAL_LIMIT = 2
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# 路径配置
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DATA_PATH = "data/income.txt"
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MODEL_PATH = "./models/income_model"
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LOG_PATH = "./logs_income"
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# 训练配置优化
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SEED = 42
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TEST_SIZE = 0.1
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EARLY_STOPPING_PATIENCE = 2
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# CPU环境配置
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MAX_GRAD_NORM = 1.0
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FP16 = False
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# CPU环境下的数据加载优化
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DATALOADER_NUM_WORKERS = 0
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DATALOADER_PIN_MEMORY = False
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# 交叉验证配置
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N_SPLITS = 3
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N_SEEDS = 1
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# 标签列表
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LABELS = [
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"O",
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"B-BANK", "I-BANK", # 银行名称
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"B-NUMBER", "I-NUMBER", # 账号尾号
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"B-DATATIME", "I-DATATIME", # 交易时间
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"B-PICKUP_CODE", "I-PICKUP_CODE", # 收入金额
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"B-BALANCE", "I-BALANCE" # 余额
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]
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# 实体长度限制
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MAX_ENTITY_LENGTH = {
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"BANK": 15, # 银行名称最大长度
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"NUMBER": 10, # 账号尾号最大长度
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"DATATIME": 20, # 时间最大长度
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"PICKUP_CODE": 20, # 收入金额最大长度
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"BALANCE": 20 # 余额最大长度
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}
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# 金额相关配置
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AMOUNT_CONFIG = {
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'max_length': 20, # 金额最大长度
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'invalid_words': ['共', '计', '额', '为', '是', '人民币', '￥', '¥', '元'],
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'valid_separators': ['.', ','], # 有效的分隔符
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'decimal_places': 2, # 小数位数
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'max_integer_digits': 12, # 整数部分最大位数
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'currency_symbols': ['￥', '¥', 'RMB', '元'], # 货币符号
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'decimal_context_range': 3 # 查找小数点的上下文范围
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}
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