Azure AI Language Project

 

Create Your Azure AI Language Project

Your Azure AI Language project can be created in one of two ways. Language Studio will offer to build a language service for you if you begin using it without first creating one in the Azure portal. Using the Azure portal to construct your language service is the most flexible approach to start an Azure AI Language project. You can add unique features if you select this option.

When constructing your language service, choose the custom feature that you will use to generate a custom text classification. Using this procedure, you will also connect the language service to a storage account.

You can go straight to the Language Studio from the language service's overview pane after the resource has been deployed. After that, you can start a brand-new, unique text classification project.

Train Your Classification Model

It requires recognized data to be trained, just like any other AI model. In order for the model to be tested, it must see examples of how to map data to a class.

You have the option to allow the model to automatically divide your training data; by default, it will utilize 20% of the documents for blind testing and 80% of the documents for training. You can designate certain documents for testing if you wish to use them to test your model.

Choose Data labeling for your project in Language Studio. All of your documents will be visible to you. After choosing each document to include in the testing set, click Testing the model's performance. Create a new training job after saving your amended labels.

Create Search Index

There isn't anything specific you need to do to create a search index that will be enriched by a custom text classification model. You will be updating the index, indexer, and custom skill after you've created a function app.

Create an Azure Function App

For your function app, you can select the technologies and language of your choice. The function then sends a structured JSON message back to a custom skillset in AI Search. The application must be able to send JSON to the custom text classification endpoint.

There are five things the function app needs to know:

1. The text to be classified.
2. The endpoint for your trained custom text classification deployed model.
3. The primary key for the custom text classification project.
4. The project name.
5. The deployment name.

The text to be classified is passed from your custom skillset in AI Search to the function as input. The remaining four items can be found in Language Studio. The endpoint and deployment name is on the deploying a model pane. The project name and primary key are on the project settings pane.

Conclusion

We have successfully learnt about Azure AI language project.