72 lines
1.6 KiB
Python
72 lines
1.6 KiB
Python
"""
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模型配置文件
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"""
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# 文本预处理参数
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MAX_SEQUENCE_LENGTH = 500 # 文本序列最大长度
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MAX_NUM_WORDS = 50000 # 词汇表最大大小
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MAX_CHAR_LENGTH = 2000 # 字符级最大长度
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MIN_WORD_FREQUENCY = 5 # 最小词频
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# 模型架构参数
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CNN_CONFIG = {
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"embedding_dim": 200,
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"num_filters": 256,
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"filter_sizes": [3, 4, 5],
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"dropout_rate": 0.5,
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"l2_reg_lambda": 0.0,
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}
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RNN_CONFIG = {
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"embedding_dim": 200,
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"hidden_size": 256,
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"num_layers": 2,
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"bidirectional": True,
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"dropout_rate": 0.5,
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}
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TRANSFORMER_CONFIG = {
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"embedding_dim": 200,
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"num_heads": 8,
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"ff_dim": 512,
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"num_layers": 4,
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"dropout_rate": 0.1,
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}
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# 针对RTX 4090的优化设置
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BATCH_SIZE = 128 # RTX 4090有24GB显存，可以支持较大的batch
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EVAL_BATCH_SIZE = 256 # 评估时可以用更大的batch
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# 训练参数
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LEARNING_RATE = 1e-3
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NUM_EPOCHS = 20
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EARLY_STOPPING_PATIENCE = 3
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REDUCE_LR_PATIENCE = 2
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REDUCE_LR_FACTOR = 0.5
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VALIDATION_SPLIT = 0.1
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TEST_SPLIT = 0.1
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# 词嵌入参数
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USE_PRETRAINED_EMBEDDING = True
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EMBEDDING_TYPE = "word2vec" # 可选: word2vec, glove, fasttext
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# 随机种子，保证实验可重复性
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RANDOM_SEED = 42
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# 模型保存参数
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SAVE_BEST_ONLY = True
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MODEL_CHECKPOINT_PATH = "best_model.h5"
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# 特征工程参数
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USE_CHAR_LEVEL = False # 是否使用字符级特征
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USE_WORD_LEVEL = True # 是否使用词级特征
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USE_TFIDF = False # 是否使用TF-IDF特征
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USE_POS_TAGS = False # 是否使用词性标注特征
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# 数据增强参数
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USE_DATA_AUGMENTATION = False
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AUGMENTATION_FACTOR = 0.2 # 增强20%的数据
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# 推理参数
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PREDICTION_THRESHOLD = 0.5
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TOP_K_PREDICTIONS = 3 |