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from sentence_transformers import SentenceTransformer
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ltable_path = r'E:\Data\Research\Projects\matching_dependency\datasets\Amazon-Google\Amazon.csv'
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rtable_path = r'E:\Data\Research\Projects\matching_dependency\datasets\Amazon-Google\GoogleProducts.csv'
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mapping_path = r'E:\Data\Research\Projects\matching_dependency\datasets\Amazon-Google\Mapping.csv'
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mapping_lid = 'idAmazon' # mapping表中左表id名
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mapping_rid = 'idGoogleBase' # mapping表中右表id名
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ltable_id = 'id' # 左表id字段名称
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rtable_id = 'id' # 右表id字段名称
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target_attr = 'id' # 进行md挖掘时的目标字段
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# lr_attrs_map = {} # 如果两个表中存在对应字段名称不一样的情况，将名称加入列表便于调整一致
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model = SentenceTransformer('E:\\Data\\Research\\Models\\roberta-large-nli-stsb-mean-tokens')
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interpre_weight = 0.5 # 可解释性权重
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# similarity_threshold = 0.2
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# support_threshold = 100
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# confidence_threshold = 0.4
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er_output_dir = 'E:\\Data\\Research\\Projects\\matching_dependency\\ml_er\\output\\'
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md_output_dir = 'E:\\Data\\Research\\Projects\\matching_dependency\\md_discovery\\output\\'
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hpo_output_dir = 'E:\\Data\\Research\\Projects\\matching_dependency\\hpo\\output\\'
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