Staging to master to add github metrics

.flake8

@@ -13,4 +13,4 @@
 # F821  undefined name 'get_ipython' --> from generated python files using nbconvert
 
 ignore = E203, E266, W503, F403, F405, E402, E731, F821
-max-line-length = 79
+max-line-length = 100

NOTICE.txt

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README.md

@@ -3,27 +3,9 @@
 | ------ | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --- | ------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
 | master | [![Build Status](https://dev.azure.com/best-practices/nlp/_apis/build/status/unit-test-master?branchName=master)](https://dev.azure.com/best-practices/nlp/_build/latest?definitionId=22&branchName=master) |     | staging | [![Build Status](https://dev.azure.com/best-practices/nlp/_apis/build/status/unit-test-staging?branchName=staging)](https://dev.azure.com/best-practices/nlp/_build/latest?definitionId=21&branchName=staging) |
 
-
 # NLP Best Practices
 
-This repository contains examples and best practices for building NLP systems, provided as Jupyter notebooks and utility functions. 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.
-
-The following section includes a list of the available scenarios. Each scenario is demonstrated in one or more Jupyter notebook examples that make use of the core code base of models and utilities.
-
-
-## Scenarios
-
-
-| Scenario                 | Applications                                 | Languages | Models |
-|---| ------------------------ | -------------------------------------------- | ------------------- |
-|[Text Classification](scenarios/text_classification)      |Topic Classification|en, zh, ar|BERT|
-|[Named Entity Recognition](scenarios/named_entity_recognition) |Wikipedia NER                                              | en, zh  |BERT|
-|[Sentence Similarity](scenarios/sentence_similarity)      |STS Benchmark                         |en|Representation: TF-IDF, Word Embeddings, Doc Embeddings<br>Metrics: Cosine Similarity, Word Mover's Distance|
-|[Embeddings](scenarios/embeddings)| Custom Embeddings Training|en|Word2Vec<br>fastText<br>GloVe|
-
-
-## Planning
-All feature planning is done via projects, milestones, and issues in this repository.
+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.
 
 ## 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.

scenarios/README.md

@@ -0,0 +1,37 @@
+# NLP Scenarios
+
+This folder contains examples and best practices, written in Jupyter notebooks, for building Natural Language Processing systems for different scenarios.
+
+## Summary
+
+The following is a summary of the scenarios covered in the best practice notebooks. Each scenario is demonstrated in one or more Jupyter notebook examples that make use of the core code base of models and utilities.
+
+| Scenario                 | Applications                                 |  Models |
+|---| ------------------------ | ------------------- |
+|[Text Classification](scenarios/text_classification)      |Topic Classification|BERT|
+|[Named Entity Recognition](scenarios/named_entity_recognition) |Wikipedia NER                                              |BERT|
+|[Question Answering](scenarios/question_answering) |SQuAD                                              | BiDAF|
+|[Sentence Similarity](scenarios/sentence_similarity)      |STS Benchmark                         |Representation: TF-IDF, Word Embeddings, Doc Embeddings<br>Metrics: Cosine Similarity, Word Mover's Distance|
+|[Embeddings](scenarios/embeddings)| Custom Embeddings Training|Word2Vec<br>fastText<br>GloVe|
+
+## Azure-enhanced notebooks
+
+Azure products and services are used in certain notebooks to enhance the efficiency of developing Natural Language systems at scale.
+
+To successfully run these notebooks, the users **need an Azure subscription** or can [use Azure for free](https://azure.microsoft.com/en-us/free/).
+
+The Azure products featured in the notebooks include:
+
+* [Azure Machine Learning service](https://azure.microsoft.com/en-us/services/machine-learning-service/) - Azure Machine Learning service is a cloud service used to train, deploy, automate, and manage machine learning models, all at the broad scale that the cloud provides. It is used across various notebooks for the AI model development related tasks like:
+  * Using Datastores
+  * Tracking and monitoring metrics to enhance the model creation process
+  * Distributed Training
+  * Hyperparameter tuning
+  * Scaling up and out on Azure Machine Learning Compute
+  * Deploying a web service to both Azure Container Instance and Azure Kubernetes Service
+
+* [Azure Kubernetes Service](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-deploy-and-where#aks) - You can use Azure Machine Learning service to host your classification model in a web service deployment on Azure Kubernetes Service (AKS). AKS is good for high-scale production deployments and provides autoscaling, and fast response times.
+
+* [Azure Container Instance](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-deploy-and-where#aci)- You can use Azure Machine Learning service to host your classification model in a web service deployment on Azure Container Instance (ACI). ACI is good for low scale, CPU-based workloads.
+
+There may be other Azure service or products used in the notebooks. Introduction and/or reference of those will be provided in the notebooks.