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Bidirectional encoder representations from transformers (BERT) is a language model introduced in October 2018 by researchers at Google. [ 1 ] [ 2 ] It learns to represent text as a sequence of vectors using self-supervised learning .
It was followed by BERT (2018), an encoder-only Transformer model. [35] In 2019 October, Google started using BERT to process search queries. [36] In 2020, Google Translate replaced the previous RNN-encoder–RNN-decoder model by a Transformer-encoder–RNN-decoder model. [37]
BERT pioneered an approach involving the use of a dedicated [CLS] token prepended to the beginning of each sentence inputted into the model; the final hidden state vector of this token encodes information about the sentence and can be fine-tuned for use in sentence classification tasks. In practice however, BERT's sentence embedding with the ...
It was followed by BERT (2018), an encoder-only Transformer model. [33] In 2019 October, Google started using BERT to process search queries. [ 34 ] In 2020, Google Translate replaced the previous RNN-encoder–RNN-decoder model by a Transformer-encoder–RNN-decoder model.
This was optimized into the transformer architecture, published by Google researchers in Attention Is All You Need (2017). [27] That development led to the emergence of large language models such as BERT (2018) [28] which was a pre-trained transformer (PT) but not designed to be generative (BERT was an "encoder-only" model).
The paper introduced the Transformer model, which eschews the use of recurrence in sequence-to-sequence tasks and relies entirely on self-attention mechanisms. The model has been instrumental in the development of several subsequent state-of-the-art models in NLP, including BERT, [7] GPT-2, and GPT-3.
A large language model (LLM) is a type of machine learning model designed for natural language processing tasks such as language generation.LLMs are language models with many parameters, and are trained with self-supervised learning on a vast amount of text.
T5 (Text-to-Text Transfer Transformer) is a series of large language models developed by Google AI introduced in 2019. [1] [2] Like the original Transformer model, [3] T5 models are encoder-decoder Transformers, where the encoder processes the input text, and the decoder generates the output text.