train.py

# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import paddle
import paddlehub as hub

if __name__ == '__main__':
    model = hub.Module(name='ernie_tiny', version='2.0.0', task='sequence_classification')

    train_dataset = hub.datasets.ChnSentiCorp(
        tokenizer=model.get_tokenizer(tokenize_chinese_chars=True), max_seq_len=128, mode='train')
    dev_dataset = hub.datasets.ChnSentiCorp(
        tokenizer=model.get_tokenizer(tokenize_chinese_chars=True), max_seq_len=128, mode='dev')
    test_dataset = hub.datasets.ChnSentiCorp(
        tokenizer=model.get_tokenizer(tokenize_chinese_chars=True), max_seq_len=128, mode='test')

    optimizer = paddle.optimizer.AdamW(learning_rate=5e-5, parameters=model.parameters())
    trainer = hub.Trainer(model, optimizer, checkpoint_dir='test_ernie_text_cls', use_gpu=True)

    trainer.train(train_dataset, epochs=3, batch_size=32, eval_dataset=dev_dataset, save_interval=1)
    trainer.evaluate(test_dataset, batch_size=32)