Model Overview The BERTBase model uses 12 layers of transformers block with a hidden size of 768 and number of self-attention heads as 12 and has around 110M trainable parameters.
What is a transformer layer in Bert?
BERT is short for Bidirectional Encoder Representations from Transformers. It is a new type of language model developed and released by Google in late 2018. BERT is a multi-layer bidirectional Transformer encoder based on fine-tuning. At this point it is important to introduce the Transformer architecture.
What is the model architecture of Bert encoder?
BERT Model Architecture: BERT Model architecture is a multi-layer bidirectional Transformer encoder-decoder structure. Encoder: Encoder is composed of a stack of N=6 identical layers. Each layer has two sub layers. The first layer is a multi-head self-attention mechanism and the second is a position wise fully connected feed-forward network.
How does Bert work as a Transformers program?
As in the Transformers, Bert will take a sequence of words (vector) as an input that keeps feed up from the first encoder layer up to the last layer in the stack. Each layer in the stack will apply the self-attention method to the sequence after that it will pass to the feed-forward network to deliver the next encoder layer.
Which is the best base model for Bert?
In Bert paper, they present two types of Bert models one is the Best Base and the other is Bert Large. Both of these models have a large number of encoder layers 12 for the base and 24 for the large.
BERT architectures (BASE and LARGE) also have larger feedforward-networks (768 and 1024 hidden units respectively), and more attention heads (12 and 16 respectively) than the Transformer architecture suggested in the original paper. It contains 512 hidden units and 8 attention heads.