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Bidirectional Encoder Representations From Transformers
Bidirectional encoder representations from transformers (BERT) is a language model introduced in October 2018 by researchers at Google. It learns to represent text as a sequence of vectors using self-supervised learning. It uses the encoder-only transformer architecture. BERT dramatically improved the state-of-the-art for large language models. , BERT is a ubiquitous baseline in natural language processing (NLP) experiments. BERT is trained by masked token prediction and next sentence prediction. As a result of this training process, BERT learns contextual, latent representations of tokens in their context, similar to ELMo and GPT-2. It found applications for many natural language processing tasks, such as coreference resolution and polysemy resolution. It is an evolutionary step over ELMo, and spawned the study of "BERTology", which attempts to interpret what is learned by BERT. BERT was originally implemented in the English language at two model sizes, BERTBASE (110 million ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Google AI
Google AI is a division of Google dedicated to artificial intelligence. It was announced at Google I/O 2017 by CEO Sundar Pichai. This division has expanded its reach with research facilities in various parts of the world such as Zurich, Paris, Israel, and Beijing. In 2023, Google AI was part of the reorganization initiative that elevated its head, Jeff Dean, to the position of chief scientist at Google. This reorganization involved the merging of Google Brain and DeepMind, a UK-based company that Google acquired in 2014 that operated separately from the company's core research. In March 2019, Google announced the creation of an Advanced Technology External Advisory Council (ATEAC) comprising eight members: Alessandro Acquisti, Bubacarr Bah, De Kai, Dyan Gibbens, Joanna Bryson, Kay Coles James, Luciano Floridi and William Joseph Burns. Following objections from a large number of Google staff to the appointment of Kay Coles James, the Council was abandoned within one ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
BookCorpus
BookCorpus (also sometimes referred to as the Toronto Book Corpus) is a Data set, dataset consisting of the text of around 7,000 self-published books web scraping, scraped from the indie ebook distribution website Smashwords. It was the main Text corpus, corpus used to train the initial generative pre-trained transformer, GPT model by OpenAI, and has been used as training data for other early large language models including Google's BERT (language model), BERT. The dataset consists of around 985 million words, and the books that comprise it span a range of genres, including romance, science fiction, and fantasy. The corpus was introduced in a 2015 paper by researchers from the University of Toronto and MIT titled "Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books". The authors described it as consisting of "free books written by yet unpublished authors," yet this is factually incorrect. These books were published by self-publishe ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
