ALBERT (A Lite BERT) is a parameter-efficient transformer-based language model designed for self-supervised pretraining on large text corpora. It uses cross-layer parameter sharing and factorized embedding factorization to reduce model size compared to standard BERT while maintaining competitive performance. The repository provides model weights in multiple sizes (base, large, xlarge, xxlarge), fine-tuning scripts for GLUE benchmarks and SQuAD question answering, and TF-Hub compatible checkpoints for easy deployment.