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 = []
