Complete Guide to ALBERT - A Lite BERT(With Python Code)

Complete Guide to ALBERT - A Lite BERT(With Python Code)

ALBERT is a lite version of BERT which shrinks down the BERT in size while maintaining the performance. It is a Multi-headed, multi-layer Transformer.

Transformer models, especially BERT transformed the NLP pipeline. They solved the problem of sparse annotations for text data. Instead of training a model from scratch, we can now simply fine-tune existing pre-trained models. But the sheer size of BERT(340M parameters) makes it a bit unapproachable. It is very compute-intensive and time taking to run inference using BERT.ALBERT is a lite version of BERT which shrinks down the BERT in size while maintaining the performance.

This model was published in a paper presented at ICLR 2020 by Zhenzhong Lan, Mingda Chen2, Sebastian Goodman, Kevin Gimpel, Piyush Sharma and Radu Soricut (researchers at Google Research and Toyota Technological Institute at Chicago). Link.

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