Language model pre-training, such as BERT, has significantly improved the performances of many natural language processing tasks. However, the pre-trained language models are usually computationally expensive and memory intensive, so it is difficult to effectively execute them on resource-restricted devices. To accelerate inference and reduce model size while maintaining accuracy, we firstly propose a novel Transformer distillation method that is specially designed for knowledge distillation (KD) of the Transformer-based models. By leveraging this new KD method, the plenty of knowledge encoded in a large “teacher” BERT can be well transferred to a small “student” TinyBERT. Moreover, we introduce a new two-stage learning framework for TinyBERT, which performs Transformer distillation at both the pre-training and task-specific learning stages. This framework ensures that TinyBERT can capture the general domain as well as the task-specific knowledge in BERT. TinyBERT is empirically effective and achieves comparable results with BERT on GLUE benchmark, while being 7.5x smaller and 9.4x faster on inference. TinyBERT is also significantly better than state-of-the-art baselines on BERT distillation, with only ∼28% parameters and ∼31% inference time of them.