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#!/usr/bin/env bash
# @Author: bo.shi, https://github.com/chineseGLUE/chineseGLUE
# @Date: 2019-11-04 09:56:36
# @Last Modified by: bright
# @Last Modified time: 2019-11-10 09:00:00
TASK_NAME="lcqmc"
MODEL_NAME="albert_tiny_zh"
CURRENT_DIR=$(cd -P -- "$(dirname -- "$0")" && pwd -P)
export CUDA_VISIBLE_DEVICES="0"
export ALBERT_CONFIG_DIR=$CURRENT_DIR/albert_config
export ALBERT_PRETRAINED_MODELS_DIR=$CURRENT_DIR/prev_trained_model
export ALBERT_TINY_DIR=$ALBERT_PRETRAINED_MODELS_DIR/$MODEL_NAME
#mkdir chineseGLUEdatasets
export GLUE_DATA_DIR=$CURRENT_DIR/chineseGLUEdatasets
# download and unzip dataset
if [ ! -d $GLUE_DATA_DIR ]; then
mkdir -p $GLUE_DATA_DIR
echo "makedir $GLUE_DATA_DIR"
fi
cd $GLUE_DATA_DIR
if [ ! -d $TASK_NAME ]; then
mkdir $TASK_NAME
echo "makedir $GLUE_DATA_DIR/$TASK_NAME"
fi
cd $TASK_NAME
echo "Please try again if the data is not downloaded successfully."
wget -c https://raw.githubusercontent.com/pengming617/text_matching/master/data/train.txt
wget -c https://raw.githubusercontent.com/pengming617/text_matching/master/data/dev.txt
wget -c https://raw.githubusercontent.com/pengming617/text_matching/master/data/test.txt
echo "Finish download dataset."
# download model
if [ ! -d $ALBERT_TINY_DIR ]; then
mkdir -p $ALBERT_TINY_DIR
echo "makedir $ALBERT_TINY_DIR"
fi
cd $ALBERT_TINY_DIR
if [ ! -f "albert_config_tiny.json" ] || [ ! -f "vocab.txt" ] || [ ! -f "checkpoint" ] || [ ! -f "albert_model.ckpt.index" ] || [ ! -f "albert_model.ckpt.meta" ] || [ ! -f "albert_model.ckpt.data-00000-of-00001" ]; then
rm *
wget https://storage.googleapis.com/albert_zh/albert_tiny_489k.zip
unzip albert_tiny_489k.zip
rm albert_tiny_489k.zip
else
echo "model exists"
fi
echo "Finish download model."
# run task
cd $CURRENT_DIR
echo "Start running..."
python run_classifier.py \
--task_name=$TASK_NAME \
--do_train=true \
--do_eval=true \
--data_dir=$GLUE_DATA_DIR/$TASK_NAME \
--vocab_file=$ALBERT_CONFIG_DIR/vocab.txt \
--bert_config_file=$ALBERT_CONFIG_DIR/albert_config_tiny.json \
--init_checkpoint=$ALBERT_TINY_DIR/albert_model.ckpt \
--max_seq_length=128 \
--train_batch_size=64 \
--learning_rate=1e-4 \
--num_train_epochs=5.0 \
--output_dir=$CURRENT_DIR/${TASK_NAME}_output/
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