update bert-leveldb

examples/bert/run_classifier_single_gpu.sh

 #!/bin/bash
-BERT_BASE_PATH="./data/pretrained_models/uncased_L-12_H-768_A-12/"
-TASK_NAME='MNLI'
+BERT_BASE_PATH="./bert_uncased_L-12_H-768_A-12/"
 DATA_PATH="./data/glue_data/MNLI/"
 CKPT_PATH="./data/saved_model/mnli_models"
 
-export CUDA_VISIBLE_DEVICES=1
+export CUDA_VISIBLE_DEVICES=0
 
 # start fine-tuning
 python3.7 bert_classifier.py\
@@ -12,7 +11,6 @@ python3.7 bert_classifier.py\
     --do_train true \
     --do_test true \
     --batch_size 64 \
-    --init_pretraining_params ${BERT_BASE_PATH}/dygraph_params/ \
     --data_dir ${DATA_PATH} \
     --vocab_path ${BERT_BASE_PATH}/vocab.txt \
     --checkpoints ${CKPT_PATH} \

examples/bert_leveldb/bert_classifier.py

@@ -159,7 +159,7 @@ def main():
         labels,
         device=device)
 
-    cls_model.bert_layer.load("./bert_small", reset_optimizer=True)
+    cls_model.bert_layer.load("./bert_uncased_L-12_H-768_A-12/bert", reset_optimizer=True)
 
     # do train
     cls_model.fit(train_data=train_dataloader.dataloader,

examples/bert_leveldb/readme.md

+0. python3.7 -m pip install leveldb
+
+1. download data: wget https://paddle-hapi.bj.bcebos.com/data/bert_data.tar.gz
+
+2. unzip data: tar -zvxf bert_data.tar.gz
+
+3. download pretrained parameters: wget https://paddle-hapi.bj.bcebos.com/models/bert_uncased_L-12_H-768_A-12.tar.gz
+
+4. unzip pretrained parameters: tar -zvxf bert_uncased_L-12_H-768_A-12.tar.gz
+
+4. bash run_classifier_single_gpu.sh 

examples/bert_leveldb/run_classifier_multi_gpu.sh

 #!/bin/bash
-BERT_BASE_PATH="./data/pretrained_models/uncased_L-12_H-768_A-12/"
-TASK_NAME='MNLI'
+BERT_BASE_PATH="./bert_uncased_L-12_H-768_A-12/"
 DATA_PATH="./data/glue_data/MNLI/"
 CKPT_PATH="./data/saved_model/mnli_models"
 
 # start fine-tuning
-python3.7 -m paddle.distributed.launch --started_port 8899 --selected_gpus=1,2,3 bert_classifier.py\
+python3.7 -m paddle.distributed.launch --started_port 8899 --selected_gpus=0,1,2,3 bert_classifier.py\
     --use_cuda true \
     --do_train true \
     --do_test true \
     --batch_size 64 \
-    --init_pretraining_params ${BERT_BASE_PATH}/dygraph_params/ \
     --data_dir ${DATA_PATH} \
     --vocab_path ${BERT_BASE_PATH}/vocab.txt \
     --checkpoints ${CKPT_PATH} \

examples/bert_leveldb/run_classifier_single_gpu.sh

 #!/bin/bash
-BERT_BASE_PATH="./data/pretrained_models/uncased_L-12_H-768_A-12/"
-TASK_NAME='MNLI'
+BERT_BASE_PATH="./bert_uncased_L-12_H-768_A-12/"
 DATA_PATH="./data/glue_data/MNLI/"
 CKPT_PATH="./data/saved_model/mnli_models"
 
@@ -12,7 +11,6 @@ python3.7 bert_classifier.py\
     --do_train true \
     --do_test true \
     --batch_size 64 \
-    --init_pretraining_params ${BERT_BASE_PATH}/dygraph_params/ \
     --data_dir ${DATA_PATH} \
     --vocab_path ${BERT_BASE_PATH}/vocab.txt \
     --checkpoints ${CKPT_PATH} \