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
