#As we initialize JointIDSF from JointBERT, user need to train a base model JointBERT first ./run_jointBERT-CRF_XLM-Rencoder.sh #Train JointIDSF export lr=3e-5 export c=0.25 export s=10 echo "${lr}" export MODEL_DIR=JointIDSF_XLM-Rencoder export MODEL_DIR=$MODEL_DIR"/"$lr"/"$c"/"$s echo "${MODEL_DIR}" python3 main.py --token_level syllable-level \ --model_type xlmr \ --model_dir $MODEL_DIR \ --data_dir PhoATIS \ --seed $s \ --do_train \ --do_eval \ --save_steps 140 \ --logging_steps 140 \ --num_train_epochs 50 \ --tuning_metric mean_intent_slot \ --use_intent_context_attention \ --attention_embedding_size 200 \ --use_crf \ --gpu_id 0 \ --embedding_type soft \ --intent_loss_coef $c \ --pretrained \ --pretrained_path JointBERT-CRF_XLM-Rencoder/4e-5/0.45/10 \ --learning_rate $lr