def test_pipeline_load_from_hdd_after_training(self):
train_df = self.load_classifier_dl_dataset()
train_df.columns = ['y','text']
pipe = nlu.load('train.classifier',verbose=True,)
pipe = pipe.fit(train_df)
store_path = t.create_model_dir_if_not_exist_and_get_path()
pipe.save(store_path, overwrite=True)
print(pipe.predict('I Love offline mode!'))
def test_saving_trained_model(self):
store_path = t.create_model_dir_if_not_exist_and_get_path()
train_df = self.load_classifier_dl_dataset().iloc[0:100]
# test_path = '/home/loan/Documents/freelancework/jsl/nlu/4realnlugit/tests/datasets/news_category_test.csv'
# store_path = '/home/loan/Documents/freelancework/jsl/nlu/4realnlugit/tmp/models'
train_df.columns = ['y', 'text']
pipe = nlu.load(
'train.classifier',
verbose=True,
)
fitted_pipe = pipe.fit(train_df)
fitted_pipe.save(store_path, overwrite=True)
def test_pipeline_save(self):
store_path = t.create_model_dir_if_not_exist_and_get_path()
nlu.load('emotion').save(store_path, overwrite=True)