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