From 895c14eefc4c1989267fcff12b4b16190751bb23 Mon Sep 17 00:00:00 2001 From: Said Bleik Date: Mon, 29 Jul 2019 02:13:02 -0400 Subject: [PATCH] readme edits --- README.md | 25 ++++++++++++++++++++++++- 1 file changed, 24 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 9c89107..5b305cb 100755 --- a/README.md +++ b/README.md @@ -5,7 +5,30 @@ # 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.