EELBERT: Dynamic Compression for Transformer Models Without Sacrificing Accuracy

Introducing EELBERT: A New Approach for AI Model Compression

EELBERT is a new approach for compressing transformer-based models like BERT, without sacrificing accuracy. By replacing the input embedding layer with dynamic embedding computations, we’re able to significantly reduce the model size. Our empirical evaluation on the GLUE benchmark shows that our BERT variants (EELBERT) experience minimal regression compared to traditional BERT models. This has allowed us to develop our smallest model, UNO-EELBERT, which achieves a GLUE score within 4% of fully trained BERT-tiny while being 15x smaller (1.2 MB) in size.

How Does EELBERT Work?

EELBERT works by replacing the input embedding layer of BERT models with dynamic embedding computations, resulting in a significant reduction in model size. This allows for minimal regression in accuracy when compared to traditional BERT models, making it a promising approach for AI model compression.

Benefits of EELBERT

The introduction of EELBERT marks a significant advancement in the field of AI model compression. By achieving minimal regression in accuracy while dramatically reducing model size, EELBERT opens up new possibilities for deploying machine learning models in resource-constrained environments. Furthermore, the development of UNO-EELBERT showcases the potential of this approach in creating highly efficient AI models.

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