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#!/bin/bash
# Finetune a BERT or pretrained ICT model using Google natural question data
# Datasets can be downloaded from the following link:
# https://github.com/facebookresearch/DPR/blob/master/data/download_data.py
WORLD_SIZE=8
DISTRIBUTED_ARGS="--nproc_per_node $WORLD_SIZE \
--nnodes 1 \
--node_rank 0 \
--master_addr localhost \
--master_port 6000"
CHECKPOINT_PATH=<Specify path for the finetuned retriever model>
# Load either of the below
BERT_LOAD_PATH=<Path of BERT pretrained model>
PRETRAINED_CHECKPOINT=<Path of Pretrained ICT model>
python -m torch.distributed.launch $DISTRIBUTED_ARGS ./tasks/main.py \
--task RET-FINETUNE-NQ \
--train_with_neg \
--train_hard_neg 1 \
--pretrained_checkpoint ${PRETRAINED_CHECKPOINT} \
--num_layers 12 \
--hidden_size 768 \
--num_attention_heads 12 \
--tensor_model_parallel_size 1 \
--tokenizer_type BertWordPieceLowerCase \
--train_data nq-train.json \
--valid_data nq-dev.json \
--save ${CHECKPOINT_PATH} \
--load ${CHECKPOINT_PATH} \
--vocab_file bert-vocab.txt \
--bert_load ${BERT_LOAD_PATH} \
--save_interval 5000 \
--log_interval 10 \
--eval_interval 20000 \
--eval_iters 100 \
--indexer_log_interval 1000 \
--faiss_use_gpu \
--DDP_impl torch \
--fp16 \
--retriever_report_topk_accuracies 1 5 10 20 100 \
--seq_length 512 \
--retriever_seq_length 256 \
--max_position_embeddings 512 \
--retriever_score_scaling \
--epochs 80 \
--micro_batch_size 8 \
--eval_micro_batch_size 16 \
--indexer_batch_size 128 \
--lr 2e-5 \
--lr_warmup_fraction 0.01 \
--weight_decay 1e-1
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