Contents
What is the class token in BERT?
The CLASS token gathers information from all the patches using Multihead Self Attention (MSA). It is basically treated the same as patch tokens but at the end when doing classification, only the hidden output from the CLASS token are used as input to the classification layer.
Why does BERT use CLS?
BERT uses special tokens [CLS] and [SEP] to understand input properly. When a task requires more than one input such as NLI and Q-A tasks, [SEP] token helps the model to understand the end of one input and the start of another input in the same sequence input.
Does BERT use Lstm?
Bidirectional LSTM is trained both from left-to-right to predict the next word, and right-to-left, to predict the previous word. But, in BERT, the model is made to learn from words in all positions, meaning the entire sentence. Further, Google also used Transformers, which made the model even more accurate.
How are tokens used in the Bert model?
In the original implementation, the token [PAD] is used to represent paddings to the sentence. When the BERT model was trained, each token was given a unique ID. Hence, when we want to use a pre-trained BERT model, we will first need to convert each token in the input sentence into its corresponding unique IDs.
How does Bert tokenization work in a corpus?
The BERT tokenization function, on the other hand, will first breaks the word into two subwoards, namely characteristic and ##ally, where the first token is a more commonly-seen word (prefix) in a corpus, and the second token is prefixed by two hashes ## to indicate that it is a suffix following some other subwords.
How is the hidden state chosen in Bert?
In BERT, the decision is that the hidden state of the first token is taken to represent the whole sentence. To achieve this, an additional token has to be added manually to the input sentence. In the original implementation, the token [CLS] is chosen for this purpose.
How are unseen tokens converted into unseen words in Bert?
However, converting all unseen tokens into [UNK] will take away a lot of information from the input data. Hence, BERT makes use of a WordPiece algorithm that breaks a word into several subwords, such that commonly seen subwords can also be represented by the model.