Biomedical　text　mining　is　becoming　increasingly　important　as　the　number　of　biomedical　documents　grow　rapidly.　Deep　learning　has　boosted　the　development　of　biomedical　text　mining　models.　However,　as　deep　learning　models　require　a　large　amount　of　training　data,　a　hierarchical　attention　based　transfer　learning　model　is　proposed　in　this　paper　for　the　question　answering　task　in　biomedical　field　which　lacks　of　sufficient　training　data.　We　adopt　BERT　(Bidirectional　Encoder　Representation　Transformers),　which　has　the　ability　to　learn　from　large-scale　unsupervised　data,　to　enrich　the　semantic　representation　in　our　model.　Especially,　the　scaled　dot-product　attention　mechanism　captures　the　question　interaction　clues　for　passage　encoding.　The　domain　adaptation　technique　of　fine-tuning　is　used　to　reinforce　the　performance,　which　penalizes　the　deviations　from　the　source　model＇s　parameters　and　remembers　the　knowledge　of　source　domain.　We　evaluate　the　system　performance　on　the　open　data　set　of　BioASQTask　B.　The　results　show　that　our　system　achieves　the　state-of-the-art　performance　without　any　handcrafted　features　and　outperforms　the　best　solution　for　factoid　questions　in　2016　and　2017　BioASQ-Task　B.