Contents
Why do we need tokenizer?
Tokenization is breaking the raw text into small chunks. Tokenization breaks the raw text into words, sentences called tokens. These tokens help in understanding the context or developing the model for the NLP. The tokenization helps in interpreting the meaning of the text by analyzing the sequence of the words.
Which tokenizer is used in Bert?
The PyTorch-Pretrained-BERT library provides us with tokenizer for each of BERTS models. Here we use the basic bert-base-uncased model, there are several other models, including much larger models. Maximum sequence size for BERT is 512, so we’ll truncate any review that is longer than this.
Which is better to tokenize do or don’t?
However, it is disadvantageous, how the tokenization dealt with the word “Don’t”. “Don’t” stands for “do not”, so it would be better tokenized as [“Do”, “n’t\\. This is where things start getting complicated, and part of the reason each model has its own tokenizer type.
Which is an example of rule based tokenization?
As can be seen space and punctuation tokenization, as well as rule-based tokenization, is used here. Space and punctuation tokenization and rule-based tokenization are both examples of word tokenization, which is loosely defined as splitting sentences into words.
How big is the vocabulary of the GPT2 tokenizer?
With some additional rules to deal with punctuation, the GPT2’s tokenizer can tokenize every text without the need for the symbol. GPT-2 has a vocabulary size of 50,257, which corresponds to the 256 bytes base tokens, a special end-of-text token and the symbols learned with 50,000 merges.
Which is the tokenization algorithm used for Bert?
WordPiece is the subword tokenization algorithm used for BERT, DistilBERT, and Electra. The algorithm was outlined in Japanese and Korean Voice Search (Schuster et al., 2012) and is very similar to BPE.