TinyBERT is a novel method for distilling BERT for natural language understanding, designed to reduce computational costs and model size while maintaining accuracy. The authors propose a new Transformer distillation method specifically for Transformer-based models, which effectively transfers knowledge from a large "teacher" BERT to a small "student" TinyBERT. They introduce a two-stage learning framework that performs Transformer distillation at both the pre-training and task-specific learning stages, ensuring that TinyBERT captures both general-domain and task-specific knowledge. Empirical results show that TinyBERT4, a 4-layer model, achieves over 96.8% of BERTBASE's performance on the GLUE benchmark, while being 7.5x smaller and 9.4x faster in inference. TinyBERT6, a 6-layer model, performs on-par with BERTBASE. The paper also includes ablation studies and comparisons with other baselines, demonstrating the effectiveness of the proposed methods.TinyBERT is a novel method for distilling BERT for natural language understanding, designed to reduce computational costs and model size while maintaining accuracy. The authors propose a new Transformer distillation method specifically for Transformer-based models, which effectively transfers knowledge from a large "teacher" BERT to a small "student" TinyBERT. They introduce a two-stage learning framework that performs Transformer distillation at both the pre-training and task-specific learning stages, ensuring that TinyBERT captures both general-domain and task-specific knowledge. Empirical results show that TinyBERT4, a 4-layer model, achieves over 96.8% of BERTBASE's performance on the GLUE benchmark, while being 7.5x smaller and 9.4x faster in inference. TinyBERT6, a 6-layer model, performs on-par with BERTBASE. The paper also includes ablation studies and comparisons with other baselines, demonstrating the effectiveness of the proposed methods.