This project involves creating a conversational language understanding model using the Azure AI Language service. The model is designed to interpret natural language input, predict user intent, and identify relevant entities. In this example, the project focuses on building a language model for a clock application, allowing users to inquire about the time, date, and day in different locations. The model is trained, tested, and deployed to a production environment, and a client application is developed in Visual Studio Code to interact with the language model.
- Azure AI Language service
- Visual Studio Code
- Git for version control
The project covers the creation of intents, labeling with sample utterances, training the language model, and incorporating learned, list, and prebuilt entities. The model is then retrained to improve its predictive performance. The client application, developed in Visual Studio Code, utilizes the Azure AI Language SDK to interact with the deployed language model, making predictions based on user input and taking appropriate actions.
Intent Definition: The project defines intents such as GetTime, GetDate, and GetDay to capture user goals.
Entity Recognition: Learned, list, and prebuilt entities are incorporated to extract relevant information from user input, enhancing context.
Model Training: The language model is trained iteratively to improve predictive performance based on labeled sample data.
Client Application: A client application is developed in Visual Studio Code, using the Azure AI Language SDK to interact with the deployed language model and generate appropriate responses based on predicted intent and entities.
Deployment: The trained model is deployed to a production environment, allowing the client application to utilize it for real-time predictions.
Testing: The client application is tested with various user inputs to evaluate the accuracy and effectiveness of the language model in understanding and responding to natural language queries.
