def load_models(self): # Run load_tagger method of all models for i, a in enumerate(self.entity_annotators.keys()): self.create_annotationset(a[0]) if a[1] == "stanfordner": model = StanfordNERModel("annotators/{}/{}".format(a[2], a[0]), a[2]) model.load_tagger(self.baseport + i) self.entity_annotators[a] = model elif a[1] == "crfsuite": model = CrfSuiteModel("annotators/{}/{}".format(a[2], a[0]), a[2]) model.load_tagger(self.baseport + i) self.entity_annotators[a] = model elif a[1] == "banner": model = BANNERModel("annotators/{}/{}".format(a[2], a[0]), a[2]) # model.load_tagger(self.baseport + i) self.entity_annotators[a] = model for i, a in enumerate(self.relation_annotators.keys()): self.create_annotationset(a[0]) if a[1] == "jsre": model = JSREKernel(None, a[2], train=False, modelname="annotators/{}/{}.model".format(a[2], a[0]), ner="all") model.load_classifier() self.relation_annotators[a] = model elif a[1] == "smil": model = MILClassifier(None, a[2], relations=[], modelname="{}.model".format(a[0]), ner="all", generate=False, test=True) model.basedir = "annotators/{}".format(a[2]) model.load_kb("corpora/transmir/transmir_relations.txt") model.load_classifier() self.relation_annotators[a] = model