What is attention mask in BERT?

What is attention mask in BERT?

It seems that the forward method of the BERT model takes as input an argument called attention_mask. The documentation says that the attention mask is an optional argument used when batching sequences together. This argument indicates to the model which tokens should be attended to, and which should not.

What is attention in BERT?

In BERT, an attention mechanism lets each token from the input sequence (e.g. sentences made of word or subwords tokens) focus on any other token. BERT uses 12 separate attention mechanism for each layer. Therefore, at each layer, each token can focus on 12 distinct aspects of other tokens.

Is BERT a sequence to sequence model?

Seq2Seq (Sequence to Sequence Translation)— uses an encoder-decoder architecture to translate between languages. BERT —this bi-directional encoder produced SOTA results in answering questions and filling in the blanks. Token masking and bi-directionality allows for exceptional context.

What does attention mask do?

The attention mask is an optional argument used when batching sequences together.

What is the difference between transformer and BERT?

BERT is only an encoder, while the original transformer is composed of an encoder and decoder. Given that BERT uses an encoder that is very similar to the original encoder of the transformer, we can say that BERT is a transformer-based model.

What does BERT look for?

BERT’s attention heads exhibit patterns such as attending to delimiter tokens, specific positional offsets, or broadly attending over the whole sentence, with heads in the same layer often exhibiting similar behaviors.

What is the difference between attention and self-attention?

The attention mechanism allows output to focus attention on input while producing output while the self-attention model allows inputs to interact with each other (i.e calculate attention of all other inputs wrt one input.

How do you make an attention mask?

The attention masks can be created as a=ayaTx. In the above figure, the top row shows ax, the column on the right shows ay and the middle rectangle shows the resulting a.

When to use the attention mask in Bert?

It seems that the forward method of the BERT model takes as input an argument called attention_mask. The documentation says that the attention mask is an optional argument used when batching sequences together. This argument indicates to the model which tokens should be attended to, and which should not.

How does attention work in a Bert transformer?

Therefore, at each layer, each token can focus on 12 distinct aspects of other tokens. Since Transformers use many distinct attention heads (12*12=144 for the base BERT model), each head can focus on a different kind of constituent combinations. We ignored the values of attention related to the “ [CLS]” and “ [SEP]” token.

How many attention mechanisms are there in Bert?

In the following illustration of an attention head, the word “it” attends to every other token and seems to focus on “street” and “animal”. Visualization of attention values on layer 0 head #1, for the token “it”. BERT uses 12 separate attention mechanism for each layer.

How to use Bert for long text input?

Here are the requirements: Add special tokens to separate sentences and do classification Pass sequences of constant length (introduce padding) Create an array of 0s (pad token) and 1s (real token) called attention mask The Transformers library provides a wide variety of Transformer models (including BERT).