用于语意匹配 input: words, ['id0', 'id1'] output: {'id0':0.2, 'id1':0.5, ...}
| 102 | |
| 103 | |
| 104 | class SemanticMatch(QAMatchBase): |
| 105 | '''用于语意匹配 |
| 106 | input: words, ['id0', 'id1'] |
| 107 | output: {'id0':0.2, 'id1':0.5, ...} |
| 108 | ''' |
| 109 | def __init__(self, words_dict, model_factory=ModelFactory, match_models=['bow', 'tfidf', 'ngram_tfidf'] ): |
| 110 | super().__init__(model_factory) |
| 111 | #with open(words_path,'r', encoding='UTF-8') as f: |
| 112 | # self.words_dict = json.load(f) |
| 113 | self._init_model( words_dict, match_models=match_models ) |
| 114 | |
| 115 | |
| 116 | def predict(self, words, id_list, match_strategy='vote', vote_threshold=0.75, key_weight = {'bow': 1, 'tfidf': 1, 'ngram_tfidf': 1}): |
| 117 | res = self._predict( words ) |
| 118 | if match_strategy == 'vote': |
| 119 | a_res_dic = self.vote(res, vote_threshold, key_weight)[1] |
| 120 | return dict(zip( id_list, [a_res_dic[i] for i in id_list] )) |
| 121 | if match_strategy == 'score': |
| 122 | a_res_dic = self.score(res, vote_threshold, key_weight)[1] |
| 123 | return dict(zip( id_list, [a_res_dic[i] for i in id_list] )) |
| 124 |
no outgoing calls