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  1.  
  2. ## **** USE 2017 DATA
  3. ## use cosine similarity as objective function
  4. def_emb_dim='300'
  5. metric_option='cosine'
  6. server='/local/datdb'
  7.  
  8. work_dir=$server/'goAndGeneAnnotationMar2017'
  9. bert_model=$work_dir/'BERT_base_cased_tune_go_branch/fine_tune_lm_bioBERT' # use the full mask + nextSentence to innit
  10. data_dir=$server/'goAndGeneAnnotationMar2017/entailment_data/AicScore/go_bert_cls'
  11. pregenerated_data=$server/'goAndGeneAnnotationMar2017/BERT_base_cased_tune_go_branch' # use the data of full mask + nextSentence to innit
  12. bert_output_dir=$pregenerated_data/'fine_tune_lm_bioBERT'
  13. mkdir $bert_output_dir
  14.  
  15. result_folder=$bert_output_dir/$metric_option'.768.reduce300ClsVec' #$def_emb_dim.'clsVec'
  16. mkdir $result_folder
  17.  
  18. conda activate tensorflow_gpuenv
  19. cd $server/GOmultitask
  20.  
  21. CUDA_VISIBLE_DEVICES=7 python3 $server/GOmultitask/BERT/encoder/do_model.py --main_dir $work_dir --qnli_dir $data_dir --batch_size_label 8 --batch_size_bert 8 --bert_model $bert_model --pregenerated_data $pregenerated_data --bert_output_dir $bert_output_dir --result_folder $result_folder --epoch 1 --num_train_epochs_entailment 25 --num_train_epochs_bert 2 --use_cuda --metric_option $metric_option --def_emb_dim $def_emb_dim --reduce_cls_vec > $result_folder/train.log
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