- BERT (Bidirectional Encoder Representations from Transformers): A natural language processing model developed by Google that is designed to help computers understand human language. Link: **https://github.com/google-research/bert**
- Transformer-XL: A natural language processing model developed by researchers at Carnegie Mellon University that is designed to generate longer sequences of text than other models. Link: **https://github.com/kimiyoung/transformer-xl**
- XLNet: A natural language processing model developed by researchers at Carnegie Mellon University and Google that is designed to address the issue of context fragmentation in language modeling. Link: **https://github.com/zihangdai/xlnet**
- RoBERTa (Robustly Optimized BERT Approach): A natural language processing model developed by Facebook that is designed to improve upon the performance of BERT. Link: **https://github.com/pytorch/fairseq/tree/master/examples/roberta**
- ALBERT (A Lite BERT): A natural language processing model developed by researchers at Google that is designed to reduce the number of parameters required for training while maintaining high performance. Link: **https://github.com/google-research/albert**
- ELECTRA (Efficiently Learning an Encoder that Classifies Token Replacements Accurately): A natural language processing model developed by researchers at Google that is designed to improve upon the efficiency and accuracy of BERT. Link: **https://github.com/google-research/electra**
- GShard (Giant Shared Model): A distributed training framework developed by researchers at Google that is designed to train large language models more efficiently. Link: **https://github.com/google-research/gshard**
- Flair: A natural language processing library developed by researchers at the Hasso Plattner Institute in Germany that includes pre-trained models for text classification, named entity recognition, and other tasks. Link: **https://github.com/flairNLP/flair**