def main(args):
if args.generate_submission:
generate_submission(args)
else:
trainer = AppTrainer(args, ner_labels)
if args.do_eda:
show_ner_datainfo(ner_labels, train_data_generator,
args.train_file, test_data_generator,
args.test_file)
elif args.do_train:
train_examples, val_examples = load_train_val_examples(args)
trainer.train(args, train_examples, val_examples)
elif args.do_eval:
_, eval_examples = load_train_val_examples(args)
model = load_model(args)
trainer.evaluate(args, model, eval_examples)
elif args.do_predict:
test_examples = load_test_examples(args)
model = load_model(args)
trainer.predict(args, model, test_examples)
save_ner_preds(args, trainer.pred_results, test_examples)
def do_predict(args):
args.model_path = args.best_model_path
test_examples = load_test_examples(args)
model = load_model(args)
trainer.predict(args, model, test_examples)
reviews_file, category_mentions_file = save_ner_preds(
args, trainer.pred_results, test_examples)
return reviews_file, category_mentions_file
def do_predict(args):
args.model_path = args.best_model_path
test_examples = load_test_examples(args)
model = load_model(args)
trainer.predict(args, model, test_examples)
save_ner_preds(args, trainer.pred_results, test_examples)