I am trying to figure how the embedding layer works for the pretrained BERT-base model. I am using pytorch and trying to dissect the following model:
import torch model = torch.hub.load('huggingface/pytorch-transformers', 'model', 'bert-base-uncased') model.embeddings
This BERT model has 199 different named parameters, of which the first 5 belong to the embedding layer (the first layer)
==== Embedding Layer ==== embeddings.word_embeddings.weight (30522, 768) embeddings.position_embeddings.weight (512, 768) embeddings.token_type_embeddings.weight (2, 768) embeddings.LayerNorm.weight (768,) embeddings.LayerNorm.bias (768,)
As I understand, the model accepts input in the shape of
[Batch, Indices] where
Batch is of arbitrary size (usually 32, 64 or whatever) and
Indices are the corresponding indices for each word in the tokenized input sentence.
Indices has a max length of 512. One input sample might look like this:
[[101, 1996, 4248, 2829, 4419, 14523, 2058, 1996, 13971, 3899, 102]]
This contains only 1 batch and is the tokenized form of the sentence "The quick brown fox jumps over the lazy dog".
word_embeddings weight will translate each number in
Indices to a vector spanned in
768 dimensions (the embedding dimension).
position_embeddings weight is used to encode the position of each word in the input sentence. Here I am confused about why this parameter is being learnt? Looking at an alternative implementation of the BERT model, the positional embedding is a static transformation. This also seems to be the conventional way of doing the positional encoding in a transformer model. Looking at the alternative implementation it uses the sine and cosine function to encode interleaved pairs in the input. I tried comparing
pe, but I cannot see any similarity. The in the last sentence under under A.2 Pre-training Procedure (page 13) the paper states
Then, we train the rest 10% of the steps of sequence of 512 to learn the positional embeddings.
Why is the positional embedding weight being learnt and not predefined?
The next layer after the positional embedding is the
token_type_embeddings. Here I am confused about how the segment label is inferred by the model. If I understand this correctly each input sentence is delimited by the
[SEP] token. In the example above there is only 1
[SEP] token and the segment label must be
0 for that sentence. But there could be a maximum of 2 segment labels. If so, will the 2 segments be handled separately or are they processed in parallel all the same as one "array"? How does the model handle multiple sentence segments?
finally the output from theese 3 embeddings are added togheter and passed through layernorm which I understand. But, are the weights in these embedding layers adjusted when fine-tuning the model to a downstream task?