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run_finetune_on_bert-base-cased.sh
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run_finetune_on_bert-base-cased.sh
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#from google.colab import drive
#drive.mount('/content/drive')
#import os
#cd /content/drive/MyDrive/Colab\ Data
#pip install git+https://github.com/huggingface/transformers
#pip install transformers datasets -qq
env TASK_NAME=CoLa
python run_glue.py \
--model_name_or_path 'bert-base-cased' \
--task_name $TASK_NAME \
--do_train \
--do_eval \
--max_seq_length 128 \
--per_device_train_batch_size 16 \
--learning_rate 1e-5 \
--num_train_epochs 3 \
--output_dir /content/drive/MyDrive/Colab\ Data/bert-base-cased-CoLa \
--logging_steps 50 \
--overwrite_output_dir \
env TASK_NAME=STSB
python run_glue.py \
--model_name_or_path 'bert-base-cased' \
--task_name $TASK_NAME \
--do_train \
--do_eval \
--max_seq_length 128 \
--per_device_train_batch_size 16 \
--learning_rate 2e-5 \
--num_train_epochs 3 \
--output_dir /content/drive/MyDrive/Colab\ Data/bert-base-cased-STSB \
--logging_steps 50 \
--overwrite_output_dir \
env TASK_NAME=RTE
python run_glue.py \
--model_name_or_path 'bert-base-cased' \
--task_name $TASK_NAME \
--do_train \
--do_eval \
--max_seq_length 128 \
--per_device_train_batch_size 32 \
--learning_rate 3e-5 \
--num_train_epochs 3 \
--output_dir /content/drive/MyDrive/Colab\ Data/bert-base-cased-RTE \
--logging_steps 5 \
--overwrite_output_dir \
env TASK_NAME=MRPC
python run_glue.py \
--model_name_or_path 'bert-base-cased' \
--task_name $TASK_NAME \
--do_train \
--do_eval \
--max_seq_length 128 \
--per_device_train_batch_size 32 \
--learning_rate 2e-5 \
--num_train_epochs 3 \
--output_dir /content/drive/MyDrive/Colab\ Data/bert-base-cased-MRPC \
--logging_steps 5 \
--overwrite_output_dir \
env TASK_NAME=SST2
python run_glue.py \
--model_name_or_path 'bert-base-cased' \
--task_name $TASK_NAME \
--do_train \
--do_eval \
--max_seq_length 128 \
--per_device_train_batch_size 32 \
--learning_rate 1e-5 \
--num_train_epochs 3 \
--output_dir /content/drive/MyDrive/Colab\ Data/bert-base-cased-SST2 \
--logging_steps 50 \
--overwrite_output_dir \
env TASK_NAME=QNLI
python run_glue.py \
--model_name_or_path 'bert-base-cased' \
--task_name $TASK_NAME \
--do_train \
--do_eval \
--max_seq_length 128 \
--per_device_train_batch_size 32 \
--learning_rate 1e-5 \
--num_train_epochs 3 \
--output_dir /content/drive/MyDrive/Colab\ Data/bert-base-cased-QNLI \
--logging_steps 50 \
--overwrite_output_dir \
env TASK_NAME=QQP
python run_glue.py \
--model_name_or_path 'bert-base-cased' \
--task_name $TASK_NAME \
--do_train \
--do_eval \
--max_seq_length 128 \
--per_device_train_batch_size 32 \
--learning_rate 5e-5 \
--num_train_epochs 1 \
--output_dir /content/drive/MyDrive/Colab\ Data/bert-base-cased-QQP \
--logging_steps 50 \
--overwrite_output_dir \
env TASK_NAME=MNLI
python run_glue.py \
--model_name_or_path 'bert-base-cased' \
--task_name $TASK_NAME \
--do_train \
--do_eval \
--max_seq_length 128 \
--per_device_train_batch_size 32 \
--learning_rate 3e-5 \
--num_train_epochs 1 \
--output_dir /content/drive/MyDrive/Colab\ Data/bert-base-cased-MNLI \
--logging_steps 50 \
--overwrite_output_dir \