  With the explosive growth of biomedical literature, designing automatic tools
to extract information from the literature has great significance in biomedical
research. Recently, transformer-based BERT models adapted to the biomedical
domain have produced leading results. However, all the existing BERT models for
relation classification only utilize partial knowledge from the last layer. In
this paper, we will investigate the method of utilizing the entire layer in the
fine-tuning process of BERT model. To the best of our knowledge, we are the
first to explore this method. The experimental results illustrate that our
method improves the BERT model performance and outperforms the state-of-the-art
methods on three benchmark datasets for different relation extraction tasks. In
addition, further analysis indicates that the key knowledge about the relations
can be learned from the last layer of BERT model.
