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Train wordpiece tokenizer

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Before we dig into the code and explain how to train the model, let’s look at how a trained model calculates its prediction. Let’s try to classify the sentence “a visually stunning rumination on love”. The first step is to use the BERT tokenizer to first split the word into tokens.
WordPiece Vs BPE. WordPiece first initializes the vocabulary to include every character present in the training data and progressively learns a given number of merge rules. In contrast to BPE, WordPiece does not choose the most frequent symbol pair, but the one that maximizes the likelihood of the training data once added to the vocabulary.
In this chapter, we will train a transformer model named KantaiBERT using the building blocks provided by Hugging Face for BERT-like models. We covered the theo. ... For example, it does not use WordPiece tokenization but goes down to byte-level Byte-Pair Encoding (BPE). This method paved the way for a wide variety of BERT and BERT-like models.
Tip. BertTokenizer, when tokenizing the sequences, would add special tokens.Important special tokens include: [SEP] and [CLS] are special tokens added by the BertTokenizer. [SEP] is needed when the task required two sequences at a time (e.g., in BERT training). [CLS], added at the beginning of the input, stand for classifier token.The embedding of this token can be seen as
NLTK Tokenize: Exercise-5 with Solution. Write a Python NLTK program to tokenize words, sentence wise. Sample Solution: Python Code : from nltk.tokenize import sent_tokenize, word_tokenize text = "Joe waited for the train. The train was late. Mary and Samantha took the bus. I looked for Mary and Samantha at the bus station."