What is the MASK token in BERT?

What is the MASK token in BERT?

Most of the time the network will see a sentence with a [MASK] token, and its trained to predict the word that is supposed to be there. But in fine-tuning, which is done after pre-training (fine-tuning is the training done by everyone who wants to use BERT on their task), there are no [MASK] tokens!

How does masking work in BERT?

How does masking work in BERT?

  • Mask 15% of input tokens: Masking in BERT doesn’t just mask one token. Instead, it randomly chooses 15% of the input tokens and masks those.
  • Mask token, correct token, or wrong token: This is where it starts to get a little weird!

What BERT predicts?

Training the language model in BERT is done by predicting 15% of the tokens in the input, that were randomly picked. These tokens are pre-processed as follows — 80% are replaced with a “[MASK]” token, 10% with a random word, and 10% use the original word.

What does BERT’s attention MASK refer to?

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 CLS token?

[CLS] is a special classification token and the last hidden state of BERT corresponding to this token (h[CLS]) is used for classification tasks. BERT uses Wordpiece embeddings input for tokens. Along with token embeddings, BERT uses positional embeddings and segment embeddings for each token.

How does BERT Pretraining work?

BERT leverages a fine-tuning based approach for applying pre-trained language models; i.e. a common architecture is trained for a relatively generic task, and then, it is fine-tuned on specific downstream tasks that are more or less similar to the pre-training task.