Principle:Gretelai Gretel synthetics Tokenizer Training
| Knowledge Sources | |
|---|---|
| Domains | Synthetic_Data, Natural_Language_Processing, Tokenization |
| Last Updated | 2026-02-14 19:00 GMT |
Overview
Tokenizer training is the process of learning a mapping between raw text characters or subword units and numerical token IDs, which is a prerequisite for any neural text generation model.
Description
Neural networks cannot operate directly on raw text. Before an LSTM or any other sequence model can be trained, the input text must be converted into a sequence of integers where each integer represents a token. Tokenizer training solves this problem by analyzing a corpus to build a vocabulary and learning the mapping rules.
The tokenizer training process in a synthetic data pipeline involves two distinct phases:
- Data annotation: The raw input data is read line by line, optionally transformed (for example, replacing field delimiters with special tokens), and written to a new training data file. This ensures the training data has a consistent format that the downstream model trainer can consume.
- Vocabulary learning: The annotated data is analyzed to build a vocabulary. Depending on the tokenizer type, this can be as simple as enumerating unique characters or as sophisticated as learning subword units via algorithms like Byte Pair Encoding (BPE) or Unigram Language Model.
Two primary strategies exist for tokenizer training:
- Character-level tokenization: Each unique character in the training data is assigned a unique ID. This produces a small vocabulary but long sequences, and is suitable for data with limited character diversity or when fine-grained control is needed.
- Subword tokenization (e.g., SentencePiece): Subword units are learned from the data, balancing vocabulary size against sequence length. This is preferred for natural language and structured data because it captures common multi-character patterns as single tokens, reducing sequence length and improving model efficiency.
Usage
Use tokenizer training when:
- Preparing data for any neural text generation model.
- Working with structured/delimited data that requires special token handling.
- Choosing between character-level granularity (small datasets, simple character sets) and subword-level granularity (large datasets, natural language).
Theoretical Basis
Character-level tokenization builds a bijective mapping:
V = {c_1, c_2, ..., c_n} where c_i are unique characters in the corpus
char2idx: c_i -> i
idx2char: i -> c_i
The vocabulary size equals the number of unique characters. This is equivalent to one-hot encoding at the character level.
SentencePiece subword tokenization uses the Unigram Language Model approach. Given a vocabulary V of candidate subword units, the probability of a sentence S is modeled as:
P(S) = product over subwords x_i in best_segmentation(S) of P(x_i)
Training iteratively prunes the vocabulary by removing tokens whose removal least reduces the overall likelihood, until the target vocab_size is reached. Key parameters include:
- vocab_size: Maximum number of tokens (default: 20,000 for SentencePiece).
- character_coverage: Fraction of characters in the training data that must be representable (default: 1.0 for full coverage).
- pretrain_sentence_count: Number of lines loaded into memory for training (default: 1,000,000).
Data annotation handles the transformation of structured data by replacing field delimiters with learnable tokens:
Input: "Alice,30,Engineer"
Annotated: "Alice<d>30<d>Engineer<n>"
where <d> is the field delimiter token and <n> is the newline token registered as user-defined symbols in the SentencePiece vocabulary.