In this article, we explore the potential of compressing transformer models to improve their inference speed while maintaining their performance in natural language processing (NLP) tasks. The dense distilled model is proposed as a means of achieving this goal without compromising on accuracy. By using a smaller version of the teacher model, the student model can learn to mimic its behavior while reducing computational resources.
Experiments conducted show that the dense distilled model can significantly improve inference speed without sacrificing performance in various NLP tasks, including language translation and text generation. This work serves as a starting point for further research on model compression techniques for Spanish language models across different NLP tasks.
To understand how this works, let’s break down the dense distillation process into its components:
- Layer loss: The loss function for each model layer is calculated by combining two terms: attention and hidden representations. Attention scores are compared to their corresponding teacher or student models using mean squared error (MSE). Similarly, the hidden representations are compared using MSE as well.
- Dense distillation: The dense distilled model uses a smaller version of the teacher model to train the student model. This allows the student model to learn the behavior of its larger counterpart while reducing computational resources.
- Multi-task learning: The student model is trained on multiple NLP tasks simultaneously, allowing it to learn the relationships between tasks and improve overall performance.
By combining these techniques, we can create a more efficient and accurate transformer model for Spanish language processing without sacrificing performance in other tasks. This work has important implications for applications that require fast and accurate language processing, such as chatbots, voice assistants, and language translation software.
