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#!/usr/bin/env bash
set -x
set -e
DIR="$( cd "$( dirname "$0" )" && cd .. && pwd )"
echo "working directory: ${DIR}"
if [ -z "$OUTPUT_DIR" ]; then
OUTPUT_DIR="${DIR}/checkpoint/biencoder_$(date +%F-%H%M.%S)"
fi
if [ -z "$DATA_DIR" ]; then
DATA_DIR="${DIR}/data/msmarco_bm25_official/"
fi
mkdir -p "${OUTPUT_DIR}"
PROC_PER_NODE=$(nvidia-smi --list-gpus | wc -l)
# python -u -m torch.distributed.launch --nproc_per_node ${PROC_PER_NODE} src/train_biencoder.py \
deepspeed src/train_biencoder.py --deepspeed ds_config.json \
--model_name_or_path intfloat/simlm-base-msmarco \
--per_device_train_batch_size 16 \
--per_device_eval_batch_size 32 \
--add_pooler False \
--t 0.02 \
--seed 1234 \
--do_train \
--fp16 \
--train_file "${DATA_DIR}/train.jsonl" \
--validation_file "${DATA_DIR}/dev.jsonl" \
--q_max_len 32 \
--p_max_len 144 \
--train_n_passages 16 \
--dataloader_num_workers 1 \
--num_train_epochs 3 \
--learning_rate 2e-5 \
--use_scaled_loss True \
--warmup_steps 1000 \
--share_encoder True \
--logging_steps 50 \
--output_dir "${OUTPUT_DIR}" \
--data_dir "${DATA_DIR}" \
--save_total_limit 2 \
--save_strategy epoch \
--evaluation_strategy epoch \
--remove_unused_columns False \
--overwrite_output_dir \
--disable_tqdm True \
--report_to none "$@"
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