def do_inference(args): prepare_output_path(args.output_dir, args.overwrite_output_dir) device, n_gpus = setup_backend(args.no_cuda) args.batch_size = args.per_gpu_eval_batch_size * max(1, n_gpus) inference_examples = process_inference_input(args.data_file) classifier = NeuralTagger.load_model(model_path=args.model_dir) classifier.to(device, n_gpus) output = classifier.inference(inference_examples, args.b) write_column_tagged_file(args.output_dir + os.sep + "output.txt", output)
def do_inference(args): prepare_output_path(args.output_dir, args.overwrite_output_dir) device, n_gpus = setup_backend(args.no_cuda) args.batch_size = args.per_gpu_eval_batch_size * max(1, n_gpus) inference_examples = process_inference_input(args.data_file) classifier = TransformerTokenClassifier.load_model(model_path=args.model_path, model_type=args.model_type, do_lower_case=args.do_lower_case, load_quantized=args.load_quantized_model) classifier.to(device, n_gpus) output = classifier.inference(inference_examples, args.max_seq_length, args.batch_size) write_column_tagged_file(args.output_dir + os.sep + "output.txt", output)