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#!/usr/bin/env bash
MODEL_TYPE=bert
MODEL_NAME_OR_PATH=bert-large-cased
VERSION=v5
MAX_NUM_QUESTIONS=8
MAX_SEQ1_LENGTH=256
MAX_SEQ2_LENGTH=128
CAND_K=3
LAMBDA=${1:-0.5}
PRIOR=${2:-nli}
MASK=${3:-0.0}
echo "lambda = $LAMBDA, prior = $PRIOR, mask = $MASK"
DATA_DIR=$PJ_HOME/data/fact_checking/${VERSION}
OUTPUT_DIR=$PJ_HOME/models/fact_checking/${VERSION}_${MODEL_NAME_OR_PATH}/${VERSION}_${MODEL_NAME_OR_PATH}_AAAI_K${CAND_K}_${PRIOR}_m${MASK}_l${LAMBDA}
NUM_TRAIN_EPOCH=7
GRADIENT_ACCUMULATION_STEPS=2
PER_GPU_TRAIN_BATCH_SIZE=8 # 4546
PER_GPU_EVAL_BATCH_SIZE=16
LOGGING_STEPS=200
SAVE_STEPS=200
python3 train.py \
--data_dir ${DATA_DIR} \
--output_dir ${OUTPUT_DIR} \
--model_type ${MODEL_TYPE} \
--model_name_or_path ${MODEL_NAME_OR_PATH} \
--max_seq1_length ${MAX_SEQ1_LENGTH} \
--max_seq2_length ${MAX_SEQ2_LENGTH} \
--max_num_questions ${MAX_NUM_QUESTIONS} \
--do_train \
--do_eval \
--evaluate_during_training \
--learning_rate 1e-5 \
--num_train_epochs ${NUM_TRAIN_EPOCH} \
--gradient_accumulation_steps ${GRADIENT_ACCUMULATION_STEPS} \
--per_gpu_train_batch_size ${PER_GPU_TRAIN_BATCH_SIZE} \
--per_gpu_eval_batch_size ${PER_GPU_EVAL_BATCH_SIZE} \
--logging_steps ${LOGGING_STEPS} \
--save_steps ${SAVE_STEPS} \
--cand_k ${CAND_K} \
--logic_lambda ${LAMBDA} \
--prior ${PRIOR} \
--overwrite_output_dir \
--temperature 1.0 \
--mask_rate ${MASK}
python3 cjjpy.py --lark "$OUTPUT_DIR fact checking training completed"
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