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The string type of the token is inconvenient to be used by models, which take numerical inputs. Now let us build a dictionary, often called vocabulary as well, to map string tokens into numerical indices starting from 0. To do so, we first count the unique tokens in all the documents from the training set, namely a corpus, and then assign a numerical index to each unique token according to its frequency. Rarely appeared tokens are often removed to reduce the complexity. Any token that does not exist in the corpus or has been removed is mapped into a special unknown token "". We optionally add a list of reserved tokens, such as “" for padding, "" to present the beginning for a sequence, and "" for the end of a sequence.

The string type of the token is inconvenient to be used by models, which take numerical inputs. Now let us build a dictionary, often called vocabulary as well, to map string tokens into numerical indices starting from 0. To do so, we first count the unique tokens in all the documents from the training set, namely a corpus, and then assign a numerical index to each unique token according to its frequency. Rarely appeared tokens are often removed to reduce the complexity. Any token that does not exist in the corpus or has been removed is mapped into a special unknown token "". We optionally add a list of reserved tokens, such as “" for padding, "" to present the beginning for a sequence, and "" for the end of a sequence.

Computer Networking: A Top-Down Approach (7th Edition)
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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The string type of the token is inconvenient to be used by models, which take numerical inputs. Now let us build a dictionary, often called vocabulary as well, to map string tokens into numerical indices starting from 0. To do so, we first count the unique tokens in all the documents from the training set, namely a corpus, and then assign a numerical index to each unique token according to its frequency. Rarely appeared tokens are often removed to reduce the complexity. Any token that does not exist in the corpus or has been removed is mapped into a special unknown token "<unk>". We optionally add a list of reserved tokens, such as “<pad>" for padding, "<bos>" to present the beginning for a sequence, and “<eos>" for the end of a sequence.
Transcribed Image Text:The string type of the token is inconvenient to be used by models, which take numerical inputs. Now let us build a dictionary, often called vocabulary as well, to map string tokens into numerical indices starting from 0. To do so, we first count the unique tokens in all the documents from the training set, namely a corpus, and then assign a numerical index to each unique token according to its frequency. Rarely appeared tokens are often removed to reduce the complexity. Any token that does not exist in the corpus or has been removed is mapped into a special unknown token "<unk>". We optionally add a list of reserved tokens, such as “<pad>" for padding, "<bos>" to present the beginning for a sequence, and “<eos>" for the end of a sequence.
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