After processing the two spans, the 1-st output vector (the vector coding for ) is passed to a separate neural network for the binary classification into and. The two spans are separated by a special token (for "separate"). The first span starts with a special token (for "classify"). Next sentence prediction: Given two spans of text, the model predicts if these two spans appeared sequentially in the training corpus, outputting either or. "my dog is happy" with probability 10%,Īfter processing the input text, the model's 4-th output vector is passed to a separate neural network, which outputs a probability distribution over its 30,000-large vocabulary.
replaced with a random word token with probability 10%,įor example, the sentence "my dog is cute" may have the 4-th token selected for prediction.replaced with a token with probability 80%,.Language modeling: 15% of tokens were selected for prediction, and the training objective was to predict the selected token given its context. Any token not appearing in its vocabulary is replaced by for "unknown".īERT was pre-trained simultaneously on two tasks: Specifically, BERT is composed of Transformer encoder layers.īERT uses WordPiece to convert each English word into an integer code. Both models were pre-trained on the Toronto BookCorpus (800M words) and English Wikipedia (2,500M words).īERT is based on the transformer architecture. A 2020 literature survey concluded that "in a little over a year, BERT has become a ubiquitous baseline in Natural Language Processing (NLP) experiments counting over 150 research publications analyzing and improving the model." īERT was originally implemented in the English language at two model sizes: (1) BERT BASE: 12 encoders with 12 bidirectional self-attention heads totaling 110 million parameters, and (2) BERT LARGE: 24 encoders with 16 bidirectional self-attention heads totaling 340 million parameters. Bidirectional Encoder Representations from Transformers ( BERT) is a family of language models introduced in 2018 by researchers at Google.
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