Transformer-based encoder-decoder models were proposed in Vaswani et al. (2017) and have recently experienced a surge of interest, e.g. Lewis et al. (2019), Raffel et al. (2019), Zhang et al. (2020), Zaheer et al. (2020), Yan et al. (2020). Similar    

🤗 Transformers has become the default library for data scientists all around the world to explore state of the art NLP models and build new NLP features. With over 5,000 pre-trained and fine-tuned models available, in over 250 languages, it is a rich playground, easily accessible whichever framework you are working in. While experimenting with models in 🤗 Transformers is easy, deploying    

A guest blog post by Hugging Face fellow Stas Bekman As recent Machine Learning models have been growing much faster than the amount of GPU memory added to newly released cards, many users are unable to train or even just load some of those huge models onto their hardware. While there is an ongoing effort to distill some of those huge models    

In the last few months, the Hugging Face team has been working hard on improving Transformers’ TensorFlow models to make them more robust and faster. The recent improvements are mainly focused on two aspects: Computational performance: BERT, RoBERTa, ELECTRA and MPNet have been improved in order to have a much faster computation time. This    

Training Your Favorite Transformers on Cloud TPUs using PyTorch / XLA The PyTorch-TPU project originated    

A guest blog post by Amog Kamsetty from the Anyscale team Huggingface Transformers recently added the Retrieval Augmented Generation (RAG) model, a new NLP architecture that leverages external documents (like Wikipedia) to augment its knowledge and achieve state of