Tokenizers
what is tokenizer
A tokenizer is a crucial component in natural language processing (NLP) and text analysis that breaks down text into smaller, manageable units called tokens. These tokens can be words, phrases, symbols, or other meaningful elements depending on the specific requirements of the application.
how tokenizer works
There are different types of tokenizer methods.Whitespace Tokenizers, Punctuation-Based Tokenizers, Word Tokenizers,Sentence Tokenizers,Character Tokenizers, N-gram Tokenizers, Regular Expression Tokenizers and
Subword Tokenizers.
Word Tokenizers
Word tokenization, also known as lexical analysis, is the process of splitting a piece of text into individual words or tokens. Word tokenization typically involves breaking the text into words based on spaces and punctuation.
Subword Tokenizers
Subword tokenization algorithms rely on the principle that frequently used words should not be split into smaller subwords, but rare words should be decomposed into meaningful subwords. A subword tokenizer is a type of tokenizer used in natural language processing (NLP) that breaks down words into smaller units or subwords. This approach is particularly useful for handling rare or out-of-vocabulary words, reducing the vocabulary size, and improving the efficiency of language models.
Common Subword Tokenization Methods
Byte-Pair Encoding (BPE)
BPE is an iterative algorithm that merges the most frequent pairs of characters or subwords in a corpus until a desired vocabulary size is reached.
WordPiece Tokenization
Similar to BPE, WordPiece builds a vocabulary of subwords based on frequency, optimizing for a balance between vocabulary size and the ability to handle rare words.
SentencePiece
SentencePiece is an unsupervised text tokenizer and detokenizer mainly designed for Neural Network-based text generation systems. It treats the input text as a sequence of Unicode characters and uses a subword model to create subwords.
references
Tokenizers
install_url to use ShareThis. Please set it in _config.yml.