Pretok

Pretok is a pre-tokenization stage used in natural language processing to transform raw text into a form that downstream tokenizers can handle consistently. It encompasses normalization, segmentation, and encoding considerations that precede the main tokenization step.

The purpose of pretok is to standardize input so that tokenization results are more predictable across languages, scripts, and data sources. By reducing variability in how text is presented to the tokenizer, pretok can help improve downstream model performance, particularly in multilingual or noisy data settings.

Common operations in pretok include Unicode normalization, case folding, whitespace normalization, and the definition of token boundaries. Additional rules may address punctuation handling, contraction expansion, and the treatment of URLs, emojis, or numeric sequences. The exact set of operations is implementation-dependent and tailored to the task and dataset.

Pretok is typically implemented as a separate component in NLP pipelines and sits between raw text and the tokenizer. Some frameworks expose pretok as an adjustable module with configurable rules, while others integrate pretok-like steps into their tokenizers.

Evaluation and criticisms of pretok focus on trade-offs between consistency and information preservation. While pretok can reduce noise and improve cross-data compatibility, overly aggressive normalization can erase meaningful distinctions and hinder tasks that rely on exact text forms. Compatibility and reproducibility are also considerations when sharing pipelines across models.

See also: tokenization, text normalization, Unicode normalization, pre-processing.