Rephrase this text: Just as with GLUE, we represent the input question and paragraph as a single packed sequence, with the question using the A embedding and the paragraph using the B embedding.
The purpose of this is to bias the representation towards the actual observed word.
To make this compatible with WordPiece tokenization, we feed each CoNLL-tokenized input word into our WordPiece tokenizer and use the hidden state corresponding to the first Where no prediction is made for X. Since the WordPiece tokenization boundaries are a known part of the input, this is done for both training and test.
QQP Quora Question Pairs is a binary classification task where the goal is to determine if two questions asked on Quora are semantically equivalent (NAME_1 et al., 2018) .
In order to obtain a fixed-dimensional pooled representation of the input sequence, we take the final hidden state (i.e., the output of the Transformer) for the first token in the input, which by construction corresponds to the the special [CLS] word embedding.
 There should be exactly 6 paragraphs separated by the markdown divider: ***.
user