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Both are cryptographic data security methods and they essentially have the same function, however they do so with differing processes and have different effects on the data they are protecting. Tokenization is a non-mathematical approach that replaces sensitive data with non-sensitive substitutes without altering the type or length of data.
A rule-based program, performing lexical tokenization, is called tokenizer, [1] or scanner, although scanner is also a term for the first stage of a lexer. A lexer forms the first phase of a compiler frontend in processing. Analysis generally occurs in one pass.
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To tokenize, in essence, is to convert a tangible or intangible asset into a digital representation on a blockchain. This digital representation is commonly referred to as a 'token.'" What is a ...
The decoder is a standard Transformer decoder. It has the same width and Transformer blocks as the encoder. It uses learned positional embeddings and tied input-output token representations (using the same weight matrix for both the input and output embeddings). It uses a byte-pair encoding tokenizer, of the same kind as used in GPT-2. English ...
The tokenizer of BERT is WordPiece, which is a sub-word strategy like byte pair encoding. Its vocabulary size is 30,000, and any token not appearing in its vocabulary is replaced by [UNK] ("unknown"). The three kinds of embedding used by BERT: token type, position, and segment type.
To a computer, a document is only a sequence of bytes. Computers do not 'know' that a space character separates words in a document. Instead, humans must program the computer to identify what constitutes an individual or distinct word referred to as a token. Such a program is commonly called a tokenizer or parser or lexer.
Word2vec is a technique in natural language processing (NLP) for obtaining vector representations of words. These vectors capture information about the meaning of the word based on the surrounding words.