LangTest is an open-source library designed to deliver safe, robust, and fair Natural Language Processing (NLP) models. It provides a comprehensive suite of tools for evaluating and improving NLP models, targeting NLP practitioners, researchers, and data scientists. The library aims to address the gap in readily available tools for ensuring responsible AI development in NLP.

How It Works

LangTest offers a unified framework for generating and executing over 60 types of tests, covering model quality aspects like robustness, bias, representation, fairness, and accuracy. It supports popular NLP frameworks such as Spark NLP, Hugging Face, and Transformers, as well as various Large Language Models (LLMs) including OpenAI, Cohere, and Azure-OpenAI. The library can also automatically augment training data based on test results for select models.

Quick Start & Requirements

• Install with: pip install langtest[transformers]
• Supports Hugging Face, Spark NLP, and various LLM providers.
• Official documentation and examples available at langtest.org.

Highlighted Details

• Generates and executes over 60 distinct test types with a single line of code.
• Tests robustness, bias, representation, fairness, and accuracy.
• Supports NER, Translation, Text-Classification, and LLM evaluations (QA, toxicity, factuality, etc.).
• Offers automatic data augmentation for select models.

Maintenance & Community

• Actively maintained by John Snow Labs with a dedicated development team.
• Community support available via Slack (#langtest channel), GitHub issues, and discussions.
• Frequent releases with new test types, tasks, and platform support are planned.

Licensing & Compatibility

• Released under the Apache License 2.0.
• Permits commercial use, modification, distribution, and private use.

Limitations & Caveats

The project is described as an "early stage open-source community project," indicating potential for ongoing development and changes.