Transformers in natural language processing (NLP) are a type of artificial intelligence (AI) that deals with making computers better understand and process natural language. They are a crucial component of the current wave of AI developments, and are increasingly used in applications such as machine translation, text summarization or text classification.
The concept of transformers comes from the way they are structured: as ‘transformation’ blocks that can be arranged in a variety of ways to suit different tasks. Each transformation block contains an input token and an output token. The input is a word vector that represents a set of properties for a word, such as its part of speech and meaning. The output is a prediction of the probability of the occurrence of a given word in a given context.
The most prominent example of transformers is Google’s BERT (Bidirectional Encoder Representations for Transformers). BERT is a model for pre-training recursive neural networks (RNN) that enable them to process signals in both directions. Other popular transformers are GPT-2, UnifiedQA, and XLNet.
Using transformers in NLP has a number of advantages. First, they can process and interpret signals from both directions, allowing for more accurate results. Second, they are quickly becoming the standard for NLP tasks that require large datasets and more complex information. Third, they can power sophisticated understanding of language, such as ranking search results more accurately and understanding context and colloquialisms.
Transformers are fast becoming an indispensable tool for NLP and AI development, and are gradually making their way into a wide range of applications. As more research is conducted and better algorithms are developed, it is likely that their popularity and usefulness will only continue to rise.
