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)
