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| import argparse | |
| from data_loader import load_and_cache_examples | |
| from trainer import Trainer | |
| from utils import MODEL_CLASSES, MODEL_PATH_MAP, init_logger, load_tokenizer, set_seed | |
| def main(args): | |
| init_logger() | |
| set_seed(args) | |
| tokenizer = load_tokenizer(args) | |
| train_dataset = load_and_cache_examples(args, tokenizer, mode="train") | |
| dev_dataset = load_and_cache_examples(args, tokenizer, mode="dev") | |
| test_dataset = load_and_cache_examples(args, tokenizer, mode="test") | |
| trainer = Trainer(args, train_dataset, dev_dataset, test_dataset) | |
| if args.do_train: | |
| trainer.train() | |
| if args.do_eval: | |
| trainer.load_model() | |
| trainer.evaluate("test") | |
| if args.do_eval_dev: | |
| trainer.load_model() | |
| trainer.evaluate("dev") | |
| if __name__ == "__main__": | |
| parser = argparse.ArgumentParser() | |
| # parser.add_argument("--task", default=None, required=True, type=str, help="The name of the task to train") | |
| parser.add_argument("--model_dir", default=None, required=True, type=str, help="Path to save, load model") | |
| parser.add_argument("--data_dir", default="./PhoATIS", type=str, help="The input data dir") | |
| parser.add_argument("--intent_label_file", default="intent_label.txt", type=str, help="Intent Label file") | |
| parser.add_argument("--slot_label_file", default="slot_label.txt", type=str, help="Slot Label file") | |
| parser.add_argument( | |
| "--model_type", | |
| default="phobert", | |
| type=str, | |
| help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()), | |
| ) | |
| parser.add_argument("--tuning_metric", default="loss", type=str, help="Metrics to tune when training") | |
| parser.add_argument("--seed", type=int, default=1, help="random seed for initialization") | |
| parser.add_argument("--train_batch_size", default=32, type=int, help="Batch size for training.") | |
| parser.add_argument("--eval_batch_size", default=64, type=int, help="Batch size for evaluation.") | |
| parser.add_argument( | |
| "--max_seq_len", default=50, type=int, help="The maximum total input sequence length after tokenization." | |
| ) | |
| parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.") | |
| parser.add_argument( | |
| "--num_train_epochs", default=10.0, type=float, help="Total number of training epochs to perform." | |
| ) | |
| parser.add_argument("--weight_decay", default=0.0, type=float, help="Weight decay if we apply some.") | |
| parser.add_argument( | |
| "--gradient_accumulation_steps", | |
| type=int, | |
| default=1, | |
| help="Number of updates steps to accumulate before performing a backward/update pass.", | |
| ) | |
| parser.add_argument("--adam_epsilon", default=1e-8, type=float, help="Epsilon for Adam optimizer.") | |
| parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.") | |
| parser.add_argument( | |
| "--max_steps", | |
| default=-1, | |
| type=int, | |
| help="If > 0: set total number of training steps to perform. Override num_train_epochs.", | |
| ) | |
| parser.add_argument("--warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps.") | |
| parser.add_argument("--dropout_rate", default=0.1, type=float, help="Dropout for fully-connected layers") | |
| parser.add_argument("--logging_steps", type=int, default=200, help="Log every X updates steps.") | |
| parser.add_argument("--save_steps", type=int, default=200, help="Save checkpoint every X updates steps.") | |
| parser.add_argument("--do_train", action="store_true", help="Whether to run training.") | |
| parser.add_argument("--do_eval", action="store_true", help="Whether to run eval on the test set.") | |
| parser.add_argument("--do_eval_dev", action="store_true", help="Whether to run eval on the dev set.") | |
| parser.add_argument("--no_cuda", action="store_true", help="Avoid using CUDA when available") | |
| parser.add_argument( | |
| "--ignore_index", | |
| default=0, | |
| type=int, | |
| help="Specifies a target value that is ignored and does not contribute to the input gradient", | |
| ) | |
| parser.add_argument("--intent_loss_coef", type=float, default=0.5, help="Coefficient for the intent loss.") | |
| parser.add_argument( | |
| "--token_level", | |
| type=str, | |
| default="word-level", | |
| help="Tokens are at syllable level or word level (Vietnamese) [word-level, syllable-level]", | |
| ) | |
| parser.add_argument( | |
| "--early_stopping", | |
| type=int, | |
| default=50, | |
| help="Number of unincreased validation step to wait for early stopping", | |
| ) | |
| parser.add_argument("--gpu_id", type=int, default=0, help="Select gpu id") | |
| # CRF option | |
| parser.add_argument("--use_crf", action="store_true", help="Whether to use CRF") | |
| # init pretrained | |
| parser.add_argument("--pretrained", action="store_true", help="Whether to init model from pretrained base model") | |
| parser.add_argument("--pretrained_path", default="./viatis_xlmr_crf", type=str, help="The pretrained model path") | |
| # Slot-intent interaction | |
| parser.add_argument( | |
| "--use_intent_context_concat", | |
| action="store_true", | |
| help="Whether to feed context information of intent into slots vectors (simple concatenation)", | |
| ) | |
| parser.add_argument( | |
| "--use_intent_context_attention", | |
| action="store_true", | |
| help="Whether to feed context information of intent into slots vectors (dot product attention)", | |
| ) | |
| parser.add_argument( | |
| "--attention_embedding_size", type=int, default=200, help="hidden size of attention output vector" | |
| ) | |
| parser.add_argument( | |
| "--slot_pad_label", | |
| default="PAD", | |
| type=str, | |
| help="Pad token for slot label pad (to be ignore when calculate loss)", | |
| ) | |
| parser.add_argument( | |
| "--embedding_type", default="soft", type=str, help="Embedding type for intent vector (hard/soft)" | |
| ) | |
| parser.add_argument("--use_attention_mask", action="store_true", help="Whether to use attention mask") | |
| args = parser.parse_args() | |
| args.model_name_or_path = MODEL_PATH_MAP[args.model_type] | |
| main(args) | |