Contents
What is Sep token in BERT?
In BERT, the decision is that the hidden state of the first token is taken to represent the whole sentence. In the “next sentence prediction” task, we need a way to inform the model where does the first sentence end, and where does the second sentence begin. Hence, another artificial token, [SEP] , is introduced.
What is the output of the BERT model?
The bert model gives us the two outputs, one gives us the [batch,maxlen,hiddenstates] and other one is [batch, hidden States of cls token].
How is BERT different from transformer?
Introduction to BERT One of the difference is BERT use bidirectional transformer (both left-to-right and right-to-left direction) rather than dictional transformer (left-to-right direction). On the other hand, both ELMo use bidirectional language model to learn the text representations.
Can you insert Sep tokens in two sentences?
No. Inserting a SEP token or not will not change the amount of information exchange between the tokens of the 2 sentences. In both case the model will compute attention based on the 2 sentences. Each sentence can see the other sentence’s tokens, no matter of the SEP.
What is the use of [ Sep ] in paper Bert?
In one way, I add special tokens and the input looks like [CLS]+s1+ [SEP] + s2 + [SEP]. In another, I make the input look like [CLS] + s1 + s2 + [SEP]. When I input them to BERT respectively, what is the difference between them? Will the s1 in second one integrate more information from s2 than the s1 in first one does?
What are the special token labels in Bert?
In BERT, They replace separator and start of sentence with special token labels. What are there corresponding values in embedding_matrix. Are they 0-vector?
Why are tokens returned in Transformers Bert model?
As you can see here, each of your inputs was tokenized and special tokens were added according your model (bert). The encode function hasn’t processed your lists properly which could be a bug or intended beheaviour depending on how you define it because their is a method for batch processing batch_encode_plus: