Using tevatron, unpushed code
bs=32
lr=7e-6
gradient_accumulation_steps=1
real_bs=$(($bs / $gradient_accumulation_steps))
echo "real_bs: $real_bs"
echo "expected_bs: $bs"
sleep 1s
epoch=5
teacher=crystina-z/monoXLMR.pft-msmarco
dataset=Tevatron/msmarco-passage && dataset_name=enMarco
output_dir=margin-mse.distill/teacher-$(basename $teacher).student-mbert.epoch-${epoch}.${bs}x2.lr.$lr.data-$dataset_name.$commit_id
mkdir -p $output_dir
CUDA_VISIBLE_DEVICES=$device WANDB_PROJECT=distill \
python examples/distill_marginmse/distil_train.py \
--output_dir $output_dir \
--model_name_or_path bert-base-multilingual-cased \
--teacher_model_name_or_path $teacher \
--save_steps 1000 \
--dataset_name $dataset \
--fp16 \
--per_device_train_batch_size $real_bs \
--gradient_accumulation_steps 4 \
--train_n_passages 2 \
--learning_rate $lr \
--q_max_len 16 \
--p_max_len 128 \
--num_train_epochs $epoch \
--logging_steps 500 \
--overwrite_output_dir \
--dataloader_num_workers 4 \
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