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In natural language processing, a word embedding is a representation of a word. The embedding is used in text analysis . Typically, the representation is a real-valued vector that encodes the meaning of the word in such a way that the words that are closer in the vector space are expected to be similar in meaning. [ 1 ]
The tokenization system must be secured and validated using security best practices [6] applicable to sensitive data protection, secure storage, audit, authentication and authorization. The tokenization system provides data processing applications with the authority and interfaces to request tokens, or detokenize back to sensitive data.
As blockchain technology becomes more popular, tokenization is commonly used to secure the ownership of assets, protect data and participate in crypto investing. However, while many users ...
Data masking can also be referred as anonymization, or tokenization, depending on different context. The main reason to mask data is to protect information that is classified as personally identifiable information, or mission critical data. However, the data must remain usable for the purposes of undertaking valid test cycles.
An embedding, or a smooth embedding, is defined to be an immersion that is an embedding in the topological sense mentioned above (i.e. homeomorphism onto its image). [ 4 ] In other words, the domain of an embedding is diffeomorphic to its image, and in particular the image of an embedding must be a submanifold .
Tokenization may refer to: Tokenization (lexical analysis) in language processing; Tokenization in search engine indexing; Tokenization (data security) in the field ...
After embedding, the vector representation is normalized using a LayerNorm operation, outputting a 768-dimensional vector for each input token. After this, the representation vectors are passed forward through 12 Transformer encoder blocks, and are decoded back to 30,000-dimensional vocabulary space using a basic affine transformation layer.
Lexical tokenization is the conversion of a raw text into (semantically or syntactically) meaningful lexical tokens, belonging to categories defined by a "lexer" program, such as identifiers, operators, grouping symbols, and data types. The resulting tokens are then passed on to some other form of processing.