metadata
datasets:
- Tevatron/msmarco-passage
Trained with Tevatron reranker branch;
script:
epoch=3
bs=32
gradient_accumulation_steps=8
real_bs=$(( $bs / $gradient_accumulation_steps ))
CUDA_VISIBLE_DEVICES=0 python examples/reranker/reranker_train.py \
--output_dir reranker_xlmr.bs-$bs.epoch-$epoch \
--model_name_or_path xlm-roberta-large \
--save_steps 20000 \
--dataset_name Tevatron/msmarco-passage \
--fp16 \
--per_device_train_batch_size $real_bs \
--gradient_accumulation_steps $gradient_accumulation_steps \
--train_n_passages 8 \
--learning_rate 5e-6 \
--q_max_len 16 \
--p_max_len 128 \
--num_train_epochs $epoch \
--logging_steps 500 \
--dataloader_num_workers 4 \
--overwrite_output_dir