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Adding AML custom skill
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‎AzureML-Custom-Skill/CONTRIBUTING.md‎

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# Contributing to Azure Cognitive Search samples
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This project welcomes contributions and suggestions. Most contributions require you to agree to a
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Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
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the rights to use your contribution. For details, visit https://cla.microsoft.com.
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When you submit a pull request, a CLA-bot will automatically determine whether you need to provide
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a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions
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provided by the bot. You will only need to do this once across all repos using our CLA.
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This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
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For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
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contact [[email protected]](mailto:[email protected]) with any additional questions or comments.
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- [Code of Conduct](#coc)
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- [Issues and Bugs](#issue)
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- [Feature Requests](#feature)
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- [Submission Guidelines](#submit)
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## <a name="coc"></a> Code of Conduct
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Help us keep this project open and inclusive. Please read and follow our [Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
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## <a name="issue"></a> Found an Issue?
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If you find a bug in the source code or a mistake in the documentation, you can help us by
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[submitting an issue](#submit-issue) to the GitHub Repository. Even better, you can
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[submit a Pull Request](#submit-pr) with a fix.
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## <a name="feature"></a> Want a Feature?
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You can *request* a new feature by [submitting an issue](#submit-issue) to the GitHub
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Repository. If you would like to *implement* a new feature, please submit an issue with
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a proposal for your work first, to be sure that we can use it.
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* **Small Features** can be crafted and directly [submitted as a Pull Request](#submit-pr).
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## <a name="submit"></a> Submission Guidelines
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### <a name="submit-issue"></a> Submitting an Issue
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Before you submit an issue, search the archive, maybe your question was already answered.
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If your issue appears to be a bug, and hasn't been reported, open a new issue.
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Help us to maximize the effort we can spend fixing issues and adding new
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features, by not reporting duplicate issues. Providing the following information will increase the
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chances of your issue being dealt with quickly:
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* **Overview of the Issue** - if an error is being thrown a non-minified stack trace helps
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* **Version** - what version is affected (e.g. 0.1.2)
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* **Motivation for or Use Case** - explain what are you trying to do and why the current behavior is a bug for you
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* **Browsers and Operating System** - is this a problem with all browsers?
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* **Reproduce the Error** - provide a live example or a unambiguous set of steps
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* **Related Issues** - has a similar issue been reported before?
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* **Suggest a Fix** - if you can't fix the bug yourself, perhaps you can point to what might be
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causing the problem (line of code or commit)
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You can file new issues by providing the above information at the corresponding repository's issues link: https://github.com/[organization-name]/[repository-name]/issues/new].
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### <a name="submit-pr"></a> Submitting a Pull Request (PR)
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Before you submit your Pull Request (PR) consider the following guidelines:
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* Search the repository (https://github.com/[organization-name]/[repository-name]/pulls) for an open or closed PR
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that relates to your submission. You don't want to duplicate effort.
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* Make your changes in a new git fork:
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* Commit your changes using a descriptive commit message
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* Push your fork to GitHub:
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* In GitHub, create a pull request
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* If we suggest changes then:
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* Make the required updates.
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* Rebase your fork and force push to your GitHub repository (this will update your Pull Request):
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```shell
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git rebase master -i
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git push -f
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```
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That's it! Thank you for your contribution!

‎AzureML-Custom-Skill/LICENSE.md‎

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MIT License
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Copyright (c) Microsoft Corporation. All rights reserved.
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE

‎AzureML-Custom-Skill/README.md‎

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---
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topic: sample
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languages:
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- python
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- REST
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name: Build and deploy a custom skill with Azure Machine Learning
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description: This sample provides a Jupyter notebook for building and training an aspect-based sentiment model with Azure Machine Learning and then deploying the model to an Azure Kubernetes cluster for integration into an AI enrichment pipeline in Azure Cognitive Search.
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products:
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- azure
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- cognitive-search
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- machine-learning
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urlFragment: custom-skill-azure-machine-learning
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---
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# Build and deploy a custom skill with Azure Machine Learning and Azure Cognitive Search
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![MIT license badge](https://img.shields.io/badge/license-MIT-green.svg)
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In this sample, you will use the [hotel reviews dataset](https://www.kaggle.com/datafiniti/hotel-reviews) (distributed under [the Creative Commons license CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/)) to create a custom skill using Azure Machine Learning to extract aspect-based sentiment from the reviews. This allows for the assignment of positive and negative sentiment within the same review to be correctly ascribed to identified entities like staff, room, lobby, or pool.
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To train the aspect-based sentiment model, you will be using the [nlp recipes repository](https://github.com/microsoft/nlp-recipes/tree/master/examples/sentiment_analysis/absa). The model will then be deployed as an endpoint on an Azure Kubernetes cluster. Once deployed, the model is added to the enrichment pipeline as a custom skill for use by the Cognitive Search service.
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There are two datasets provided. If you wish to train the model yourself, the `hotel_reviews_1000.csv` file is required. Prefer to skip the training step? Download the `hotel_reviews_100.csv`.
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## Contents
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| File/folder | Description |
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|-------------|-------------|
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| `custom_skill_azureml.ipynb` | Jupyter notebook |
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| `datasets` | Small and large datasets for use in this process |
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| `model` | lexicon files for defining entities and opinions |
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| `CONTRIBUTING.md` | Guidelines for contributing to the sample. |
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| `LICENSE.md` | The license for the sample. |
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| `README.md` | This README file. |
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## Prerequisites
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* Azure subscription - get a [free subscription](https://azure.microsoft.com/free/?WT.mc_id=A261C142F).
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* [Cognitive Search service](https://docs.microsoft.com/azure/search/search-get-started-arm)
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* [Cognitive Services resource](https://docs.microsoft.com/azure/cognitive-services/cognitive-services-apis-create-account?tabs=multiservice%2Cwindows)
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* [Azure Storage account](https://docs.microsoft.com/azure/storage/common/storage-account-create?toc=%2Fazure%2Fstorage%2Fblobs%2Ftoc.json&tabs=azure-portal)
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* [Azure Machine Learning workspace](https://docs.microsoft.com/azure/machine-learning/how-to-manage-workspace)
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## Setup
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1. Clone or download the contents of this repository.
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1. Extract contents if the download is a zip file. Make sure the files are read-write.
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1. While setting up the Azure accounts and services, copy the names and keys to an easily accessed text file. The names and keys will be added to the first cell in the notebook where variables for accessing the Azure services are defined.
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1. If you are unfamiliar with Azure Machine Learning and its requirements, you will want to review these documents before getting started:
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* [Configure a development environment for Azure Machine Learning](https://docs.microsoft.com/azure/machine-learning/how-to-configure-environment)
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* [Create and manage Azure Machine Learning workspaces in the Azure portal](https://docs.microsoft.com/azure/machine-learning/how-to-manage-workspace)
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* When configuring the development environment for Azure Machine Learning, consider using the [cloud-based compute instance](https://docs.microsoft.com/azure/machine-learning/how-to-configure-environment#compute-instance) for speed and ease in getting started.
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1. Upload the dataset file to a container in the storage account. The [larger file](datasets\hotel_reviews_1000.csv) is necessary if you wish to perform the training step in the notebook. If you prefer to skip the training step, the [smaller file](datasets\hotel_reviews_100.csv) is recommended.
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### Running the tutorial
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* The notebook in this sample is detailed in the tutorial [Build and deploy a custom skill with Azure Machine Learning](https://docs.microsoft.com/azure/search/cognitive-search-tutorial-aml-custom-skill).
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* Open the Jupyter notebook and enter all of the subscription, account names, and keys required in the first cell of the notebook. This will allow the notebook to access the Azure based services and perform the processes in each cell of the notebook.
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* Once you have completed the exercise in the notebook, delete your resource group to avoid additional costs.
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## Next steps
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You can learn more about Azure Cognitive Search on the [official documentation site](https://docs.microsoft.com/azure/search/).

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