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It uses the encoder-only transformer architecture. It is notable for its dramatic improvement over previous state-of-the-art models, and as an early example of a large language model. As of 2020, BERT is a ubiquitous baseline in natural language processing (NLP) experiments. [3]
Many large language models, since they do not need to predict a whole new sequence from an input sequence, only use the encoder or decoder of the original transformer architecture. Early GPT models are decoder-only models trained to predict the next token in a sequence. [ 58 ]
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.
An early and influential language model. [6] Encoder-only and thus not built to be prompted or generative. [7] Training took 4 days on 64 TPUv2 chips. [8] T5: October 2019: Google 11 [9] 34 billion tokens [9] Apache 2.0 [10] Base model for many Google projects, such as Imagen. [11] XLNet: June 2019: Google: 0.340 [12] 33 billion words 330 ...
The first one ("encoder") takes in image patches with positional encoding, and outputs vectors representing each patch. The second one (called "decoder", even though it is still an encoder-only Transformer) takes in vectors with positional encoding and outputs image patches again. During training, both the encoder and the decoder ViTs are used.
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). Also in 2018, OpenAI published Improving Language Understanding by Generative Pre-Training, which introduced GPT-1, the first in its GPT series. [29]
2. encoder-decoder QKV 3. encoder-only dot product 4. encoder-only QKV 5. Pytorch tutorial Both encoder & decoder are needed to calculate attention. [42] Both encoder & decoder are needed to calculate attention. [48] Decoder is not used to calculate attention. With only 1 input into corr, W is an auto-correlation of dot products. w ij = x i x j ...
Though the original transformer has both encoder and decoder blocks, BERT is an encoder-only model. Academic and research usage of BERT began to decline in 2023, following rapid improvements in the abilities of decoder-only models (such as GPT) to solve tasks via prompting. [13]
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