## init from some checkpoint, to resume the previous training if args.init_from_checkpoint: transformer.load(args.init_from_checkpoint) ## init from some pretrain models, to better solve the current task if args.init_from_pretrain_model: transformer.load(args.init_from_pretrain_model, reset_optimizer=True) # model train transformer.fit(train_data=train_loader, eval_data=eval_loader, epochs=args.epoch, eval_freq=1, save_freq=1, save_dir=args.save_model, callbacks=[ TrainCallback(args, train_steps_fn=train_steps_fn, eval_steps_fn=eval_steps_fn) ]) if __name__ == "__main__": args = PDConfig(yaml_file="./transformer.yaml") args.build() args.Print() check_gpu(args.use_cuda) check_version() do_train(args)
LacLoss(), ChunkEval(num_labels), inputs=inputs, labels=labels, device=args.device) if args.init_from_checkpoint: model.load(args.init_from_checkpoint) if args.init_from_pretrain_model: model.load(args.init_from_pretrain_model, reset_optimizer=True) model.fit(train_dataset.dataloader, epochs=args.epoch, batch_size=args.batch_size, eval_freq=args.eval_freq, save_freq=args.save_freq, save_dir=args.save_dir) if __name__ == '__main__': args = PDConfig(yaml_file="sequence_tagging.yaml") args.build() args.Print() use_gpu = True if args.device == "gpu" else False check_gpu(use_gpu) check_version() main(args)