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README.md

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 # NLP Best Practices
 
-This repository contains examples and best practices for building NLP systems, provided as [Jupyter notebooks](scenarios) and [utility functions](utils_nlp). The focus of the repository is on state-of-the-art methods and common scenarios that are popular among researchers and practitioners working on problems involving text and language.
+This repository contains examples and best practices for building natural language processing (NLP) systems, provided as [Jupyter notebooks](scenarios) and [utility functions](utils_nlp). The focus of the repository is on state-of-the-art methods and common scenarios that are popular among researchers and practitioners working on problems involving text and language.
+
+![](https://nlpbp.blob.core.windows.net/images/cognitive_services.PNG)
+## Overview
+
+The goal of this repository is to build a comprehensive set of tools and examples that leverage recent advances in NLP algorithms, neural architectures, and distributed machine learning systems. 
+The content is based on our past and potential future engagements with customers as well as collaboration with partners, researchers, and the open source community.
+
+We’re hoping that the tools would significantly reduce the time from a business problem, or a research idea, to full implementation of a system. In addition, the example notebooks would serve as guidelines and showcase best practices and usage of the tools.
+
+In an era of transfer learning, transformers, and deep architectures, we believe that pretrained models provide a unified solution to many real-world problems and allow handling different tasks and languages easily. We will, therefore, prioritize such models, as they achieve state-of-the-art results on several NLP benchmarks and can be used in a number of applications ranging from simple text classification to sophisticated intelligent chat bots.
+
+> [*GLUE Leaderboard*](https://gluebenchmark.com/leaderboard)  
+> [*SQuAD Leaderbord*](https://rajpurkar.github.io/SQuAD-explorer/)
+
+
+The following is a list of typical scenarios that we aim at covering.
+-	Text Classification
+-	Named Entity Recognition
+-	Text Similarity/Matching
+-	Question Answering
+-	Text Summarization
+-	Machine Translation
+
 
 ## Getting Started
 To get started, navigate to the [Setup Guide](SETUP.md), where you'll find instructions on how to setup your environment and dependencies.