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tokenizer
In natural language processing, this term refers to the critical first stage of a data pipeline. It transforms raw strings into a structured format that a machine learning model can interpret. The choice of tokenizer—whether word-based, character-based, or subword-based (like Byte Pair Encoding)—significantly impacts the model's ability to handle unknown words and vocabulary size.
In the context of cybersecurity and fintech, the term describes a security mechanism. Here, it functions as a proxy system that swaps sensitive data for a surrogate value. This ensures that if a database is breached, the stolen tokens are useless to an attacker because they lack the original sensitive mapping stored in a secure vault.
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Ví dụ
The new tokenizer handles emojis and special characters much more efficiently.
We need to update the tokenizer to support multiple languages.
I wonder if a different tokenizer would reduce the number of tokens per sentence.
The compiler uses a tokenizer to convert the source code into a stream of tokens.
Our payment gateway uses a secure tokenizer to protect customer credit card numbers.
Does this tokenizer comply with PCI DSS security standards?
The system implements a tokenizer to ensure that sensitive data never hits the local database.
Wait, is the tokenizer splitting the words correctly?
A robust tokenizer is essential for any high-performance natural language processing pipeline.
A robust tokenizer is essential for any high-performance natural language processing pipeline.