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