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| #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 |