embed-bge-m3/FlagEmbedding/examples/finetune/reranker/encoder_only/base.sh

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export WANDB_MODE=disabled
train_data="\
../example_data/normal/examples.jsonl "
# set large epochs and small batch size for testing
num_train_epochs=4
per_device_train_batch_size=2
gradient_accumulation_steps=1
train_group_size=8
# set num_gpus to 2 for testing
num_gpus=2
if [ -z "$HF_HUB_CACHE" ]; then
export HF_HUB_CACHE="$HOME/.cache/huggingface/hub"
fi
model_args="\
--model_name_or_path BAAI/bge-reranker-base \
--cache_dir $HF_HUB_CACHE \
"
data_args="\
--train_data $train_data \
--cache_path ~/.cache \
--train_group_size $train_group_size \
--query_max_len 256 \
--passage_max_len 256 \
--pad_to_multiple_of 8 \
--knowledge_distillation True \
"
training_args="\
--output_dir ./test_encoder_only_base_bge-reranker-base \
--overwrite_output_dir \
--learning_rate 6e-5 \
--fp16 \
--num_train_epochs $num_train_epochs \
--per_device_train_batch_size $per_device_train_batch_size \
--gradient_accumulation_steps $gradient_accumulation_steps \
--dataloader_drop_last True \
--warmup_ratio 0.1 \
--gradient_checkpointing \
--weight_decay 0.01 \
--deepspeed ../../ds_stage0.json \
--logging_steps 1 \
--save_steps 1000 \
"
cmd="torchrun --nproc_per_node $num_gpus \
-m FlagEmbedding.finetune.reranker.encoder_only.base \
$model_args \
$data_args \
$training_args \
"
echo $cmd
eval $cmd