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You are here		joeddav.github.io Zero-Shot Learning in Modern NLP \| Joe Davison Blog
\|	\|	explosion.ai Embed, encode, attend, predict: The new deep learning formula for state-of-the-art NLP models · Explosion	3.3 parsecs away Travel
\|	\|	Over the last six months, a powerful new neural network playbook has come together for Natural Language Processing. The new approach can be summarised as a simple four-step formula: embed, encode, attend, predict. This post explains the components of this new approach, and shows how they're put together in two recent systems.	3.3 parsecs away Travel
\|	\|	towardsml.wordpress.com BERT Explained: A Complete Guide with Theory and Tutorial - Towards Machine Learning	3.2 parsecs away Travel
\|	\|	Unless you have been out of touch with the Deep Learning world, chances are that you have heard about BERT - it has been the talk of the town for the last one year. At the end of 2018 researchers at Google AI Language open-sourced a new technique for Natural Language Processing (NLP) called BERT...	3.2 parsecs away Travel
\|	\|	blog.paperspace.com ReFT: Representation Finetuning for Language Models	3.9 parsecs away Travel
\|	\|	In this article we introduce pyreft, a novel fine-tuning method called Representation Fine-Tuning (ReFT), which offers superior efficiency and interpretability compared to state-of-the-art methods like PEFTs.	3.9 parsecs away Travel
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