Release PR: v0.3.1

README.md

@@ -10,7 +10,7 @@
 [![](https://img.shields.io/badge/arXiv-2407.10853-B31B1B.svg)](https://arxiv.org/abs/2407.10853)
 
 
-LangFair is a comprehensive Python library designed for conducting bias and fairness assessments of large language model (LLM) use cases. This repository includes a comprehensive framework for [choosing bias and fairness metrics](https://github.com/cvs-health/langfair/tree/main#choosing-bias-and-fairness-metrics-for-an-llm-use-case), along with [demo notebooks](https://github.com/cvs-health/langfair/tree/main/examples) and a [technical playbook](https://arxiv.org/abs/2407.10853) that discusses LLM bias and fairness risks, evaluation metrics, and best practices. 
+LangFair is a comprehensive Python library designed for conducting bias and fairness assessments of large language model (LLM) use cases. This repository includes a comprehensive framework for [choosing bias and fairness metrics](https://github.com/cvs-health/langfair/tree/main#-choosing-bias-and-fairness-metrics-for-an-llm-use-case), along with [demo notebooks](https://github.com/cvs-health/langfair/tree/main/examples) and a [technical playbook](https://arxiv.org/abs/2407.10853) that discusses LLM bias and fairness risks, evaluation metrics, and best practices. 
 
 Explore our [documentation site](https://cvs-health.github.io/langfair/) for detailed instructions on using LangFair.
 

examples/evaluations/classification/classification_metrics_demo.ipynb

@@ -78,7 +78,7 @@
    "source": [
     "#### Classification Metrics\n",
     "***\n",
-    "##### `ClassificationMetrics()` - For calculating FaiRLLM (Fairness of Recommendation via LLM) metrics (class)\n",
+    "##### `ClassificationMetrics()` - Pairwise classification fairness metrics (class)\n",
     "\n",
     "**Class parameters:**\n",
     "- `metric_type` - (**{'all', 'assistive', 'punitive', 'representation'}, default='all'**) Specifies which metrics to use.\n",