Using Neural Architecture Search to Compress BERT Models for Improved Performance and Faster Inference
Main Ideas:
- Neural architecture search (NAS) based structural pruning can be used to compress fine-tuned BERT models.
- Pre-trained language models (PLMs) are being widely adopted in areas such as productivity tools, customer service, search and recommendations, and business process automation.
- Structural pruning through NAS can improve model performance and reduce inference times.
Author’s Take:
NAS-based structural pruning provides a powerful solution for compressing BERT models without sacrificing performance. With the increasing adoption of PLMs in various industries, the ability to improve model performance and reduce inference times is crucial. This technique opens up new possibilities for more efficient and effective deployment of language models in real-world applications.