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run_sup.sh
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run_sup.sh
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#!/bin/bash
# In this example, we show how to train SimCSE using multiple GPU cards and PyTorch's distributed data parallel on supervised NLI dataset.
# Set how many GPUs to use
#NUM_GPU=3
# Randomly set a port number
# If you encounter "address already used" error, just run again or manually set an available port id.
#PORT_ID=$(expr $RANDOM + 1024)
# Allow multiple threads
#export OMP_NUM_THREADS=8
# Use distributed data parallel
# If you only want to use one card, uncomment the following line and comment the line with "torch.distributed.launch"
#python -m torch.distributed.launch --nproc_per_node $NUM_GPU --master_port $PORT_ID train.py \
python train.py \
--model_name_or_path /home/chen/SimCSE/my-sup-simcse-bert-base-hard_neg0-batch-512-jacsts-top4\
--train_file /home/chen/SimCSE/data/nli_for_simcse.csv \
--output_dir transfer_model/my-sup-simcse-bert-base-hard_neg1-batch-512-jacsts-top4-plusnli\
--num_train_epochs 2 \
--per_device_train_batch_size 128\
--gradient_accumulation_steps 4\
--learning_rate 5e-5 \
--max_seq_length 32 \
--evaluation_strategy steps \
--save_strategy steps \
--save_steps 125 \
--eval_steps 125 \
--pooler_type cls \
--overwrite_output_dir \
--temp 0.05 \
--hard_negative_weight 1 \
--do_train \
--do_eval \
#--fp16 \
"$@"