def __init__(self):
self.config = Config()
if "glove" in self.config.WORD_VEC_MODEL_PATH:
self.w2vmodel = w2VModel(self.config.WORD_VEC_MODEL_PATH, False)
else:
self.w2vmodel = w2VModel(self.config.WORD_VEC_MODEL_PATH)
self.embDim = 200
self.aim = 'train' # train or test.
# our vocabulary dictionary, dictionary value is 0 means padding idx.
self.cateVocab = {}
# self.path = self.config.get_train_path()
# self.path1 = self.config.TEST_FILE_PATH
# our vocabulary dictionary, dictionary value is 0 means padding idx.
self.vocabToOld = {}
self.vocabToNew = {}
self.train_data, self.train_label = self.readFile(
self.config.get_train_path())
self.test_data, self.test_label = self.readFile(
self.config.get_test_path())
self.data = self.train_data + self.test_data
self.label = self.train_label + self.test_label
self.buildVocab()
self.numVocab = len(self.w2vmodel.w2vmodel.vocab)
# [self.testData, self.testLabel] = self.readFile(testDataPath)
self.weight = self.getW2V()
def main():
config = Config()
config.running_mode = "test"
config.list_all_member()
word_dict = geniaDataset()
model = AttentionNestedNERModel(config, word_dict).cuda() if config.cuda else AttentionNestedNERModel(config,
word_dict)
config.test_data, config.test_str, config.test_label = data_prepare(config, config.get_test_path(), word_dict)
del word_dict
start_test(config, model)