<ul><li>ALBERT (A Lite BERT) is a lighter and more efficient version of BERT designed to reduce computational complexity and memory usage while maintaining performance.</li><li>ALBERT addresses BERT's limitations of parameter redundancy and memory limitation by employing factorized embedding parameterization, cross-layer parameter sharing, and introducing the Sentence Order Prediction (SOP) loss.</li><li>ALBERT achieves comparable or superior results to BERT on NLP benchmarks while using significantly fewer parameters, making it suitable for research and real-world applications with memory and computational constraints.</li><li>Practical applications of ALBERT include sentiment analysis, question answering (QA), and named entity recognition (NER), benefiting from its speed and memory efficiency.</li></ul>

Day 33 - ALBERT (A Lite BERT): Efficient Language Model

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