Blogs by Ashwin

  Azure AI Language Project Life Cycle Creating an entity extraction model typically follows a similar path to most Azure AI Language service features: Define entities : Understanding the data and entities you want to identify, and try to make them as clear as possible. For example, defining exactly which parts of a bank statement you want to extract. Tag data: Label, or tag, your existing data, describing what text in your dataset relates to which entity. This phase is vital to accomplish precisely and thoroughly, as any improper or missed labels will reduce the effectiveness of the trained model. A good variation of possible input documents is useful. For example, label bank name, customer name, customer address, specific loan or account terms, loan or account amount, and account number. Train model: Train your model once your entities are labeled. Training teaches your model how to recognize the entities you label. View model: After your model is trained, view the results ...

  Introduction One of the many Natural Language Processing (NLP) features provided by the Azure AI Language service is custom Named Entity Recognition (NER), also referred to as custom entity extraction. Developers can extract preset entities from text documents, like legal agreements or online advertisements, even if those documents don't follow a predetermined format thanks to Custom NER. A person, place, thing, event, ability, or value is an entity. Understand Custom Named Entity Recognition An Azure API service called Custom NER examines documents to find and extract user-defined entities. Names, locations, bank statements, and knowledge mining to enhance search results could all be examples of these things. The Azure AI Language in Azure AI services includes Custom NER. Custom vs built-in NER Azure AI Language has built-in entity identification capabilities to identify objects like people, places, organizations, or URLs. You can extract entities and build up the service with l...

  Deployment Options Each project can produce numerous models and deployments, each with a distinct name, thanks to Azure AI Language. Benefits include ability to: Test two models side by side Compare how the split of datasets impact performance Deploy multiple versions of your model During deployment you can choose the name for the deployed model, which can then be selected when submitting a classification task. Using REST API CLI development of Azure AI is made possible by the REST API for the Azure AI Language service. Language projects in the same manner as Language Studio offers a project-building user interface. The lab for this module delves deeper into Language Studio. Pattern of Using API For the majority of calls, the Azure AI Language service's API runs asynchronously. In each stage, we first send a request to the service and then follow up with a call to find out the status or outcome. With each request, a header is required to authenticate your request. Submit Initial ...

  Introduction Your workspace for developing, honing, refining, and implementing your classification model is custom text classification projects. Language Studio and the REST API are the two methods you can work on your project. The lab will use Language Studio as the GUI, but the REST API offers the same features. The processes for creating your model are the same regardless of your preferred approach. Azure AI Language Project Life Cycle Define Labels: Understanding the data you want to classify, identify the possible labels you want to categorize into. Tag Data : Tag, or label, your existing data, specifying the label or labels each file falls under. Labeling data is important since it's how your model will learn how to classify future files. Best practice is to have clear differences between labels to avoid ambiguity, and provide good examples of each label for the model to learn from. Train Model : Train your model with the labeled data. View Model : After your model is...

  Introduction You may process natural language for your own application by using the Azure AI Language service. One of the most prevalent AI issues is Natural Language Processing (NLP), which requires software to perceive voice or text in the same way that people do. Text classification is a component of Natural Language Processing (NLP), and Azure offers methods for text classification that include sentiment, language, and user-defined custom categories. Understand Types of Classification Projects Text files are given labels by custom text classification, which in the Azure AI Language service is a class that the developer defines. A video game synopsis, for instance, could be categorized as "Adventure," "Strategy," "Action," or "Sports." Custom text classification falls into two types of projects: Single Label Classification - you can assign only one class to each file. Following the above example, a video game summary could only be classifie...

  Use Patterns To Differentiate Similar Utterances A model may occasionally have several intents for which utterances are expected to be similar. While reducing the quantity of sample utterances, you can leverage the pattern of utterances to clarify the intents. Additionally, the intents could apply to a wide range of entity values. To correctly train your model, provide a handful of examples of each intent that specify the different formats of utterances. TurnOnDevice: "Turn on the {DeviceName}" "Switch on the {DeviceName}" "Turn the {DeviceName} on" GetDeviceStatus: "Is the {DeviceName} on[?]" TurnOffDevice: "Turn the {DeviceName} off" "Switch off the {DeviceName}" "Turn off the {DeviceName}" When you teach your model with each different type of utterance, the Azure AI Language service can learn how to categorize intents correctly based off format and punctuation. Use Pre-Built Entity Components To recognize common...