Transformers for classification
Transformer models are trained as language models. These are a type of algorithm that has been trained by analyzing patterns of human language to understand and produce human language.
They have knowledge of grammar, syntax, and semantics, and can discern patterns and connections among words and phrases. Moreover, they can detect named entities, such as individuals, locations, and establishments, and interpret the context in which they are referenced. Essentially, a transformer model is a computer program that uses statistical models to analyze and generate language.
Language models are trained in a self-supervised manner on large amounts of text data, such as books, articles, and online content, to learn patterns and relationships between words and phrases. Some of the popular datasets used for pretraining transformers include Common Crawl, Wikipedia, and BooksCorpus. For example, BERT was trained using around 3.5 billion words in total with around...