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run_long_n2c2_2018.sh
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run_long_n2c2_2018.sh
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export DATASET_NAME=n2c2_2018_track1
export TOKENIZERS_PARALLELISM=false
# Baselines (bert-base-uncased, bert-base-cased, PubmedBERT, ClinicalBERT)
CUDA_VISIBLE_DEVICES=0 python run_nlu.py \
--model_name_or_path bert-base-uncased \
--dataset_name $DATASET_NAME \
--do_train \
--do_eval \
--logging_steps 10 \
--max_seq_length 512 \
--per_device_train_batch_size 8 \
--gradient_accumulation_steps 1 \
--learning_rate 5e-5 \
--num_train_epochs 80 \
--evaluation_strategy epoch \
--output_dir ./save/bert-base-uncased-$DATASET_NAME/ \
--overwrite_output_dir
CUDA_VISIBLE_DEVICES=0 python run_nlu.py \
--model_name_or_path bert-base-cased \
--dataset_name $DATASET_NAME \
--do_train \
--do_eval \
--logging_steps 10 \
--max_seq_length 512 \
--per_device_train_batch_size 8 \
--gradient_accumulation_steps 1 \
--learning_rate 5e-5 \
--num_train_epochs 80 \
--evaluation_strategy epoch \
--output_dir ./save/bert-base-cased-$DATASET_NAME/ \
--overwrite_output_dir
CUDA_VISIBLE_DEVICES=0 python run_nlu.py \
--model_name_or_path microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract \
--dataset_name $DATASET_NAME \
--do_train \
--do_eval \
--logging_steps 10 \
--max_seq_length 512 \
--per_device_train_batch_size 8 \
--gradient_accumulation_steps 1 \
--learning_rate 5e-5 \
--num_train_epochs 80 \
--evaluation_strategy epoch \
--output_dir ./save/pubmed-bert-$DATASET_NAME/ \
--overwrite_output_dir
CUDA_VISIBLE_DEVICES=0 python run_nlu.py \
--model_name_or_path emilyalsentzer/Bio_ClinicalBERT \
--dataset_name $DATASET_NAME \
--do_train \
--do_eval \
--logging_steps 10 \
--max_seq_length 512 \
--per_device_train_batch_size 8 \
--gradient_accumulation_steps 1 \
--learning_rate 5e-5 \
--num_train_epochs 80 \
--evaluation_strategy epoch \
--output_dir ./save/clinical-bert-$DATASET_NAME/ \
--overwrite_output_dir
# Bio-LM
CUDA_VISIBLE_DEVICES=0 python run_nlu.py \
--model_name_or_path bio-lm \
--dataset_name $DATASET_NAME \
--do_train \
--do_eval \
--logging_steps 10 \
--max_seq_length 514 \
--per_device_train_batch_size 8 \
--gradient_accumulation_steps 1 \
--learning_rate 5e-5 \
--num_train_epochs 80 \
--evaluation_strategy epoch \
--output_dir ./save/bio-lm-514-$DATASET_NAME/ \
--overwrite_output_dir
CUDA_VISIBLE_DEVICES=0 python run_nlu.py \
--model_name_or_path bio-lm \
--dataset_name $DATASET_NAME \
--do_train \
--do_eval \
--logging_steps 10 \
--max_seq_length 1028 \
--per_device_train_batch_size 4 \
--gradient_accumulation_steps 2 \
--learning_rate 5e-5 \
--num_train_epochs 80 \
--evaluation_strategy epoch \
--output_dir ./save/bio-lm-1028-$DATASET_NAME/ \
--overwrite_output_dir
CUDA_VISIBLE_DEVICES=0 python run_nlu.py \
--model_name_or_path bio-lm \
--dataset_name $DATASET_NAME \
--do_train \
--do_eval \
--logging_steps 10 \
--max_seq_length 2056 \
--per_device_train_batch_size 4 \
--gradient_accumulation_steps 2 \
--learning_rate 5e-5 \
--num_train_epochs 80 \
--evaluation_strategy epoch \
--output_dir ./save/bio-lm-2056-$DATASET_NAME/ \
--overwrite_output_dir
CUDA_VISIBLE_DEVICES=0 python run_nlu.py \
--model_name_or_path bio-lm \
--dataset_name $DATASET_NAME \
--do_train \
--do_eval \
--logging_steps 10 \
--max_seq_length 4112 \
--per_device_train_batch_size 2 \
--gradient_accumulation_steps 4 \
--learning_rate 5e-5 \
--num_train_epochs 80 \
--evaluation_strategy epoch \
--output_dir ./save/bio-lm-4112-$DATASET_NAME/ \
--overwrite_output_dir
CUDA_VISIBLE_DEVICES=0 python run_nlu.py \
--model_name_or_path bio-lm \
--dataset_name $DATASET_NAME \
--do_train \
--do_eval \
--logging_steps 10 \
--max_seq_length 8224 \
--per_device_train_batch_size 1 \
--gradient_accumulation_steps 8 \
--learning_rate 5e-5 \
--num_train_epochs 80 \
--evaluation_strategy epoch \
--output_dir ./save/bio-lm-9224-$DATASET_NAME/ \
--overwrite_output_dir
# Bioelectra
CUDA_VISIBLE_DEVICES=0 python run_nlu.py \
--model_name_or_path kamalkraj/bioelectra-base-discriminator-pubmed \
--dataset_name $DATASET_NAME \
--do_train \
--do_eval \
--logging_steps 10 \
--max_seq_length 512 \
--per_device_train_batch_size 8 \
--gradient_accumulation_steps 1 \
--learning_rate 5e-5 \
--num_train_epochs 80 \
--evaluation_strategy epoch \
--output_dir ./save/bioelectra-512-$DATASET_NAME/ \
--overwrite_output_dir
CUDA_VISIBLE_DEVICES=0 python run_nlu.py \
--model_name_or_path kamalkraj/bioelectra-base-discriminator-pubmed \
--dataset_name $DATASET_NAME \
--do_train \
--do_eval \
--logging_steps 10 \
--max_seq_length 1024 \
--per_device_train_batch_size 4 \
--gradient_accumulation_steps 2 \
--learning_rate 5e-5 \
--num_train_epochs 80 \
--evaluation_strategy epoch \
--output_dir ./save/bioelectra-1024-$DATASET_NAME/ \
--overwrite_output_dir
CUDA_VISIBLE_DEVICES=0 python run_nlu.py \
--model_name_or_path kamalkraj/bioelectra-base-discriminator-pubmed \
--dataset_name $DATASET_NAME \
--do_train \
--do_eval \
--logging_steps 10 \
--max_seq_length 2048 \
--per_device_train_batch_size 4 \
--gradient_accumulation_steps 2 \
--learning_rate 5e-5 \
--num_train_epochs 80 \
--evaluation_strategy epoch \
--output_dir ./save/bioelectra-2048-$DATASET_NAME/ \
--overwrite_output_dir
CUDA_VISIBLE_DEVICES=0 python run_nlu.py \
--model_name_or_path kamalkraj/bioelectra-base-discriminator-pubmed \
--dataset_name $DATASET_NAME \
--do_train \
--do_eval \
--logging_steps 10 \
--max_seq_length 4096 \
--per_device_train_batch_size 2 \
--gradient_accumulation_steps 4 \
--learning_rate 5e-5 \
--num_train_epochs 80 \
--evaluation_strategy epoch \
--output_dir ./save/bioelectra-4192-$DATASET_NAME/ \
--overwrite_output_dir
CUDA_VISIBLE_DEVICES=0 python run_nlu.py \
--model_name_or_path kamalkraj/bioelectra-base-discriminator-pubmed \
--dataset_name $DATASET_NAME \
--do_train \
--do_eval \
--logging_steps 10 \
--max_seq_length 8192 \
--per_device_train_batch_size 1 \
--gradient_accumulation_steps 8 \
--learning_rate 5e-5 \
--num_train_epochs 80 \
--evaluation_strategy epoch \
--output_dir ./save/bioelectra-8192-$DATASET_NAME/ \
--overwrite_output_dir