Tokenization: Transforming Text Into Tokens for NLP and Generative AI Models

Tokenization is the process of breaking text into smaller units called tokens, which can represent words, subwords, or characters. It is a foundational step in Natural Language Processing (NLP) and Generative AI, enabling models to interpret language in a structured way and convert text into numerical representations suitable for machine learning algorithms.

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Key Types of Tokenization

• Word-Level Tokenization
  Splits text into complete words. Simple and intuitive, but limited for morphologically rich or low-resource languages.
  
  
• Subword Tokenization
  Breaks words into smaller meaningful units (prefixes, roots, suffixes). Methods like BPE (Byte Pair Encoding), WordPiece, and SentencePiece support out-of-vocabulary handling and efficient vocabulary control.
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• Character-Level Tokenization
  Represents every character as a token. Useful for languages without spacing rules, but leads to long sequences and higher computational load.

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Mechanisms and Algorithms

• Whitespace & Rule-Based Tokenization
  Splits text based on language rules, spacing, or punctuation.
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• Byte Pair Encoding (BPE)
  Iteratively merges frequent character pairs to form compact subword vocabularies. Formula for merging step:

Vt+1=Vt∪{ab}V_{t+1} = V_t \cup \{ab\}Vt+1​=Vt​∪{ab}

Where ab is the most frequent pair in VtV_tVt​.
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• ‍WordPiece & SentencePiece
  Tokenize based on data frequency and semantic structure, widely used in BERT, GPT, T5, and multilingual models.

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Mathematical Representation

Given an input sequence:

X={x1,x2,...,xm}X = \{x_1, x_2, ..., x_m\}X={x1​,x2​,...,xm​}

Tokenization transforms it into:

T={t1,t2,...,tn},n≤mT = \{t_1, t_2, ..., t_n\}, \quad n \leq mT={t1​,t2​,...,tn​},n≤m

Each token tit_iti​ is later mapped to an embedding vector viv_ivi​ for model processing.

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Tokenization in NLP Models

• Embedding Mapping
  Token IDs are converted into vectors representing semantic meaning in high-dimensional space.
  
  
• Handling Out-of-Vocabulary (OOV)
  Subword tokenization decomposes new or rare words, preventing unknown-token outputs.
  
  
• Interaction with Attention Mechanisms
  Transformers leverage tokenized sequences so each token can reference others via self-attention, supporting contextual understanding.
  
  

Related Terms

• Embeddings