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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)
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