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From all the docs I read, people push this way and that way on how BERT uses or generates embedding. I GET that there is a key and a query and a value and those are all generated.

What I don't know is if the original embedding - the original thing you put into BERT - could or should be a vector. People wax poetic about how BERT or ALBERT can't be used for word to word comparisons, but nobody says explicitly what bert is consuming. Is it a vector? If so is it just a one-hot vector? Why is it not a GLoVE vector? (ignore the positional encoding discussion for now please)

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  • $\begingroup$ Is not a one-hot vector ... so is part of encoder the tokenizer step does use ids vector from embedded table table generated similar as done with word2vec model (learned representation). So you have 3 phases: 1 takes input sentence/sequence and tokenize it, 2 places special tokens for beginning and ending of sequence and then 3. Does get the ids from embedded table and feed into Bert (transformer with mask to predict masked words). $\endgroup$ – n1tk Apr 29 at 1:35
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BERT cannot use GloVe embeddings, simply because it uses a different input segmentation. GloVe works with the traditional word-like tokens, whereas BERT segments its input into subword units called word-pieces. On one hand, it ensures there are no out-of-vocabulary tokens, on the other hand, totally unknown words get split into characters and BERT probably cannot make much sense of them either.

Anyway, BERT learns its custom word-piece embeddings jointly with the entire model. They cannot carry the same type of semantic information as word2vec or GloVe because they are often only word fragments and BERT needs to make sense of them in the later layers.

You might say that inputs are one-hot vectors if you want, but as almost always, it is just a useful didactic abstraction. All modern deep learning frameworks implement embedding lookup just by direct indexing, multiplying the embedding matrix with a one-hot-vector would be just wasteful.

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BERT's embeddings are 3 things :

  • Token embeddings
  • Segment embeddings
  • Position embeddings

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I guess your question is about token embeddings.

Token embeddings is a vector, where each token is encoded as a vocabulary ID.

Example :

# !pip install transformers

from transformers import BertTokenizer

t = BertTokenizer.from_pretrained("bert-base-uncased")

text = "My dog is cute."
text2 = "He likes playing"

t1 = t.tokenize(text)
t2 = t.tokenize(text2)
print(t1, t2)

tt = t.encode(t1, t2)
print(tt)

['my', 'dog', 'is', 'cute', '.'] ['he', 'likes', 'playing']

[101, 2026, 3899, 2003, 10140, 1012, 102, 2002, 7777, 2652, 102]

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