​​Scaling Transformer to 1M tokens and beyond with RMT Imagin | Data Science by ODS.ai 🦜

​​Scaling Transformer to 1M tokens and beyond with RMT

Imagine extending the context length of BERT, one of the most effective Transformer-based models in natural language processing, to an unprecedented two million tokens! This technical report unveils the Recurrent Memory Transformer (RMT) architecture, which achieves this incredible feat while maintaining high memory retrieval accuracy.

The RMT approach enables storage and processing of both local and global information, allowing information flow between segments of the input sequence through recurrence. The experiments showcase the effectiveness of this groundbreaking method, with immense potential to enhance long-term dependency handling in natural language understanding and generation tasks, as well as enable large-scale context processing for memory-intensive applications.

Paper link: https://arxiv.org/abs/2304.11062
Code link: https://github.com/booydar/t5-experiments/tree/scaling-report

A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-rmt-1m

#deeplearning #nlp #bert #memory