args.hidden_dim = 50 args.num_classes = 2 args.input_dim = 300 args.sparse = "" args.lr = 0.01 args.wd = 1e-4 args.data = os.path.join(base_path, "data/lc_quad/") args.cuda = False parser = LC_QaudParser() kb = parser.kb base_dir = "./output" question_type_classifier_path = os.path.join(base_dir, "question_type_classifier") utility.makedirs(question_type_classifier_path) question_type_classifier = SVMClassifier( os.path.join(question_type_classifier_path, "svm.model")) o = Orchestrator(None, question_type_classifier, None, parser, True) raw_entities = [{ "surface": "", "uris": [{ "confidence": 1, "uri": "http://dbpedia.org/resource/Bill_Finger" }] }] entities = [] for item in raw_entities: uris = [
ds = LC_Qaud_Linked(path="./data/LC-QUAD/linked_test.json") ds.load() ds.parse() if not ds.parser.kb.server_available: logger.error("Server is not available. Please check the endpoint at: {}".format(ds.parser.kb.endpoint)) sys.exit(0) output_file = 'lcquadtestanswer_output' linker = Earl(path="data/LC-QUAD/entity_lcquad_test.json") base_dir = "./output" question_type_classifier_path = os.path.join(base_dir, "question_type_classifier") double_relation_classifier_path = os.path.join(base_dir, "double_relation_classifier") utility.makedirs(question_type_classifier_path) utility.makedirs(double_relation_classifier_path) question_type_classifier = SVMClassifier(os.path.join(question_type_classifier_path, "svm.model")) double_relation_classifier = SVMClassifier(os.path.join(double_relation_classifier_path, "svm.model")) stats = Stats() parser = LC_QaudParser() kb = parser.kb o = Orchestrator(logger, question_type_classifier, double_relation_classifier, parser, question_type_classifier_path, True) tmp = [] output = [] na_list = []