csqa_3_eval_adapter.sh

#!/usr/bin/env bash

#Step1:
#run_classifier_adapter_tune_all.py ->
#
#<!-- Comment -->
#Need to load the Adapter Model
#Here it is probably recommended to use the orginal optimiser as it optimises BERT
TRAINING_UTILITY=training_utility

export CUDA_VISIBLE_DEVICES=0

BERT_DIR="models/BERT_BASE_UNCASED"
BERT_CONFIG=$BERT_DIR/bert_config.json
BERT_VOCAB=$BERT_DIR/vocab.txt

BERT_EXTENDED_DIR="models/omcs_pretraining_free_wo_nsp_adapter"
OUTPUT_DIR="models/output_model_finetunning"
OUTPUT_SUFFIX=_tune_all

TASKNAME='COMMONSENSEQA'
DATA_DIR=data/$TASKNAME

SPLIT="rand"

STEP="25000"

CHECKPOINT=${BERT_EXTENDED_DIR}/model.ckpt-${STEP}

TRAINED_MODEL=$OUTPUT_DIR/$TASKNAME/model.ckpt-3000

python3.6 $TRAINING_UTILITY/run_commonsenseqa_adapter.py \
  --split=$SPLIT \
  --do_train=false \
  --do_eval=true \
  --data_dir=$DATA_DIR \
  --vocab_file=$BERT_VOCAB \
  --bert_config_file=$BERT_CONFIG \
  --init_checkpoint=$TRAINED_MODEL \
  --max_seq_length=128 \
  --train_batch_size=8 \
  --learning_rate=2e-5 \
  --num_train_epochs=3.0 \
  --output_dir=$OUTPUT_DIR/$TASKNAME/ | tee $OUTPUT_DIR/$TASKNAME.out