def load_train_data(): kg_embedding_model = triple2vec(20, "../Model/kg_embedding_model/dbpedia_model/e_tr/") label2id = load_label2id() train_triples = [] train_labels = [] for label, content in [line.strip().split('<#>') for line in codecs.open(hp.train_path, 'r', 'utf-8').readlines() if line]: train_triples.append(content.strip()) train_labels.append(label2id[label]) trans_X, kg_embd_X, Y, Sources, Targets = create_data(train_triples, train_labels, kg_embedding_model) return trans_X, kg_embd_X, Y
def load_test_data(): kg_embedding_model = triple2vec(20, hp.kg_embd_model_dir) label2id = load_label2id() train_triples = [] train_labels = [] for label, content in [line.strip().split('<#>') for line in codecs.open(hp.test_path, 'r', 'utf-8').readlines() if line]: train_triples.append(content.strip()) train_labels.append(label2id[label]) # print('len', len(train_sentences), len(train_targets)) X, Y, Sources, Targets = create_data(train_triples, train_labels, kg_embedding_model) return X, Y