Blogs by Ashwin

  Introduction You may develop clever apps and services that can translate text between languages with the help of the Translator service. The capacity of speakers of various languages to communicate with one another is frequently a crucial prerequisite for global solutions, and there are numerous widely spoken languages in the globe. An API for translating text between 90 recognized languages is offered by the Azure AI Translator.   Provision An Azure AI Translator Resource Azure AI Translator provides a multilingual text translation API that you can use for: Language detection. One-to-many translation. Script transliteration (converting text from its native script to an alternative script).   Azure Resource For Azure AI Translator You need to allocate a resource in your Azure subscription in order to use the Azure AI Translator service. The Text Analytics API can be used in a multi-service Azure AI Services resource, or you can provision a single-s...

  Label Your Data Properly labeling or tagging your data is a crucial step in developing a custom entity extraction model. Labels serve to indicate instances of particular entities within the text that are used for training the model. Three things to focus on are: Consistency - Label your data the same way across all files for training. Consistency allows your model to learn without any conflicting inputs. Precision - Label your entities consistently, without unnecessary extra words. Precision ensures only the correct data is included in your extracted entity. Completeness - Label your data completely, and don't miss any entities. Completeness helps your model always recognize the entities present. How To Label Your Data ? Language Studio offers a straightforward approach for annotating your data. It enables you to view the file, mark the start and end of your entities, and specify their type. Each label you identify is saved in a file located in your storage account alongs...

  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...