CodemixedNLP: An Extensible and Open NLP Toolkit for Code-Mixing

TL;DR

CodemixedNLP is an open-source toolkit designed to facilitate research and development in code-mixed NLP, offering tools for model development, benchmarking, and analysis across multiple tasks and languages.

Contribution

It introduces a comprehensive, extensible library that consolidates recent advances in code-mixed NLP and provides resources for expanding datasets and evaluating models.

Findings

Provides fine-tuned models for 7 Hinglish tasks

Includes tools for analyzing mixing styles

Supports development and benchmarking of models

Abstract

The NLP community has witnessed steep progress in a variety of tasks across the realms of monolingual and multilingual language processing recently. These successes, in conjunction with the proliferating mixed language interactions on social media have boosted interest in modeling code-mixed texts. In this work, we present CodemixedNLP, an open-source library with the goals of bringing together the advances in code-mixed NLP and opening it up to a wider machine learning community. The library consists of tools to develop and benchmark versatile model architectures that are tailored for mixed texts, methods to expand training sets, techniques to quantify mixing styles, and fine-tuned state-of-the-art models for 7 tasks in Hinglish. We believe this work has a potential to foster a distributed yet collaborative and sustainable ecosystem in an otherwise dispersed space of code-mixing research. The toolkit is designed to be simple, easily extensible, and resourceful to both researchers as well as practitioners.