Which embedding is used in BERT?
As discussed, BERT base model uses 12 layers of transformer encoders, each output per token from each layer of these can be used as a word embedding!
What are positional encodings?
A positional encoding is a finite dimensional representation of the location or “position” of items in a sequence. Given some sequence A = [a_0, …, a_{n-1}], the positional encoding must be some type of tensor that we can feed to a model to tell it where some value a_i is in the sequence A.
How are positional and segment embeddings learned in Bert?
The original BERT paper states that unlike transformers, positional and segment embeddings are learned. What exactly does this mean? How do positional embeddings help in predicting masked tokens? Is the positional embedding of the masked token predicted along with the word? How has this been implemented in the huggingface library?
When do you need to learn an embedding?
An embedding is an embedding and as such always needs to be learned or transfered. Section 3.5 of the paper ‘ Attention is All You Need ‘ explains the positional encoding in the case of transformers. They use ‘sine and cosine functions of different frequencies’ to inject information about the position of the tokens.
Why are positional and segment embeddings so important?
The output of all three embeddings are summed up before passing them to the transformer layers. Positional embeddings can help because they basically highlight the position of a word in the sentence. A word in the first position likely has another meaning/function than the last one.
Why are positional embeddings important in a sentence?
Positional embeddings can help because they basically highlight the position of a word in the sentence. A word in the first position likely has another meaning/function than the last one. Also, the same word likely will have a different syntactic function in the first vs. last position.