Tokenpar

Tokenpar is a framework and data model for attaching metadata to tokens within text processing workflows. The term is used in discussions of tokenization, linguistic annotation, and reproducible NLP experiments to denote the parameterization of tokens with features such as lemmas, part-of-speech tags, morphological attributes, and user-defined annotations.

Core features include a standardized token representation, a configuration interface to define fields, and adapters that integrate with common tokenizers. It supports streaming pipelines, multi-language tokenization, and serialization of token metadata to formats like JSON and JSONL. The design emphasizes portability and interoperability across tools and languages.

In typical workflows, a tokenizer produces tokens; tokenpar enriches them with attributes according to a schema; downstream components such as parsers, taggers, or evaluators ingest the enriched token stream. The framework supports versioned schemas and configuration files to promote reproducibility and data provenance, and it enables consistent token metadata across different stages of an NLP pipeline.

History and reception notes describe tokenpar as arising from efforts to standardize token metadata across NLP toolchains. It is referenced in discussions of robust evaluation, dataset annotation, and model interpretability. Community resources often include documentation, example schemas, and language-specific adapters, aimed at helping researchers and developers implement interoperable token annotations in diverse projects.