|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
export WANDB_PROJECT=distilbart-trainer |
|
export BS=32 |
|
export m=sshleifer/student_cnn_12_6 |
|
export tok=facebook/bart-large |
|
export MAX_TGT_LEN=142 |
|
|
|
python finetune_trainer.py \ |
|
--model_name_or_path $m --tokenizer_name $tok \ |
|
--data_dir cnn_dm \ |
|
--output_dir distilbart-cnn-12-6 --overwrite_output_dir \ |
|
--learning_rate=3e-5 \ |
|
--warmup_steps 500 --sortish_sampler \ |
|
--fp16 \ |
|
--n_val 500 \ |
|
--gradient_accumulation_steps=1 \ |
|
--per_device_train_batch_size=$BS --per_device_eval_batch_size=$BS \ |
|
--freeze_encoder --freeze_embeds \ |
|
--num_train_epochs=2 \ |
|
--save_steps 3000 --eval_steps 3000 \ |
|
--logging_first_step \ |
|
--max_target_length 56 --val_max_target_length $MAX_TGT_LEN --test_max_target_length $MAX_TGT_LEN\ |
|
--do_train --do_eval --do_predict \ |
|
--evaluation_strategy steps \ |
|
--predict_with_generate --sortish_sampler \ |
|
"$@" |
|
|