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How GitHub Copilot Helps with Test-Driven Development (TDD)
Test-Driven Development (TDD) has been a cornerstone of modern software engineering for decades. By writing tests before implementing functionality, developers ensure better design, maintainability, and fewer bugs. But while TDD is powerful, it can sometimes feel slow or cumbersome, especially when setting up repetitive test structures or boilerplate code. This is where GitHub Copilot, the AI-powered coding assistant, becomes a valuable partner. It doesn’t replace the discipline of TDD, but it can accelerate the process and help developers stay in the flow. https://dellenny.com/how-github-copilot-helps-with-test-driven-development-tdd/Boosting Productivity with GitHub Copilot Real-World .NET Coding Examples
Software development in the .NET ecosystem is powerful, but projects can quickly get bogged down with boilerplate code, repetitive tasks, and lengthy setup. This is where GitHub Copilot comes in. Acting as your AI pair programmer inside Visual Studio or VS Code, Copilot suggests context-aware code snippets, tests, and even full method implementations. Let’s dive into real-world .NET coding examples that highlight how Copilot can make you more productive. https://dellenny.com/boosting-productivity-with-github-copilot-real-world-net-coding-examples/
ML.NET Sentence Similarity scenario questions
How do you make the ML.NET Sentence Similarity scenario context-aware? I'm using the Model Builder to train, evaluate, and consume the model. I would like to create a context prior to comparing sentences. Is this possible? Is there a way to introduce this in the pipeline? Does a model in ML.NET rely entirely on the training dataset? Is there a pre-trained dataset behind the scenarios? Training a Sentence Similarity model only works when you use validation dataset. Using Cross validation or the Split option always returns this error. "Training time finished without completing a successful trial. Either no trial completed or the metric for all completed trials are NaN or Infinity" Is this a bug?
Gpt3 model for spelling correction is not working for every sentances
I have used GPT-3 model for correcting the spelling mistakes in questions we are providing to the bot. Used the below code for connecting to model, var requestData = new { prompt = $"Correct the spelling in the following text: '{question}'", max_tokens = 50, temperature = 0.7, stop = "\n" }; string apiKey = "open ai key"; string endpoint = "https://api.openai.com/v1/engines/davinci/completions"; using (HttpClient client = new HttpClient()) { client.DefaultRequestHeaders.Add("Authorization", $"Bearer {apiKey}"); var requestData1 = new { prompt = prompt, max_tokens = 50, }; var content = new StringContent(Newtonsoft.Json.JsonConvert.SerializeObject(requestData1), Encoding.UTF8, "application/json"); var response = await client.PostAsync(endpoint, content); GPT-3 provides the corrected text for few words. But it does not provide the corrected text for long sentences.
ML.Net CLI Example Fails
On following the instructions here: https://learn.microsoft.com/en-us/dotnet/machine-learning/tutorials/sentiment-analysis-cli at this step mlnet classification --dataset "yelp_labelled.txt" --label-col 1 --has-header false --train-time 10 I get this error: System.InvalidOperationException: Unable to resolve service for type 'Microsoft.ML.AutoML.IDatasetManager' while attempting to activate 'Microsoft.ML.AutoML.SweepablePipelineRunner'. at Microsoft.Extensions.DependencyInjection.ServiceLookup.CallSiteFactory.CreateArgumentCallSites(Type implementationType, CallSiteChain callSiteChain, ParameterInfo[] parameters, Boolean throwIfCallSiteNotFound) at Microsoft.Extensions.DependencyInjection.ServiceLookup.CallSiteFactory.CreateConstructorCallSite(ResultCache lifetime, Type serviceType, Type implementationType, CallSiteChain callSiteChain) ... ... mlnet --version says 16.15.0 Anyone know what's wrong?
How to create mini search engine by using ML.Net with c#?
I have some medicine names in Sql server database table. Now I want to create some feature by using ML.Net c#? I am totally new to ML.NET. What I want is that ASAP user try to write anything in my textbox (.Aspx page), my ML.Net feature should suggest the user. for example : user write "penadol 40 mg" or "panadl" or "Pandole" and there can be some other patterns as well. My ML.Net logic would suggest the user "Panadol" by finding from the database. Something, very similar to English spell check feature or mini search engine Now I am going to tell you about the structure of development. Currently , my application is developed in aspx , As soon as user 'll enter some medicine name in the textbox, an ajax request 'll go to the server and 'll display the suggested medicine list. Ajax call can be requested to an API which 'll be used this ML.NET developed feature. And this API can be developed in any .Net/ .Net Core Framework . Please guide me . Thank you very much. Please ask anything if want to further know anything from me.
Dataset for toxic comments detection
Hello there, I have a website that contains a list of resources (basically other websites). Users can leave their reviews for each resource, a review consists of Title, Text and Rate (number 0-5). I want to implement toxic and offensive reviews detection using ML .Net. I don't have experience of work with ML, but I've checked intro course from MS (https://dotnet.microsoft.com/en-us/learn/ml-dotnet/get-started-tutorial/intro) and as far as I understand I need a dataset with textual reviews and their classes (like 0 - for toxic and 1 - for normal). I will be thankful if you advise my further steps, share tutorials etc. Also can you advise such datasets? Or provide a link to github repo with working code? Thanks.Consume Azure Custom Vision ONNX Models with ML.NET
Azure Custom Vision is an image recognition service that lets you build, deploy, and improve your own computer vision models. Custom Vision allows you to specify your own labels and train custom machine models using your data. Custom Vision supports training models for the following tasks: Image classification Object Detection Once you've trained a model, Custom Vision provides you with a variety of formats to export and deploy your model to. One of those formats is the Open Neural Network Exchange (ONNX). ONNX is an open-source format to represent AI models. ONNX models can be used to make predictions with the help of the ONNX Runtime (ORT). The ORT is a runtime for ONNX models which provides an interface for accelerating the consumption / inferencing of machine learning models, integrating with hardware-specific libraries, and sharing models across programming languages and frameworks like PyTorch, Tensorflow / Keras, scikit-learn, Windows ML, ML.NET, and others. ML.NET is an open-source, cross-platform machine learning framework for .NET developers. ML.NET provides a set of APIs that build on the ONNX Runtime. That means you can take models you've exported from Custom Vision and use them inside your .NET applications. Get started with Custom Vision and ML.NET To get started using Custom Vision models with ML.NET Train an image classification or object detection model. Export your model to ONNX Use and reference these sample applications which show how to build an ML.NET pipeline that consumes a Custom Vision ONNX model. Resources What is Custom Vision? ONNX website ONNX Runtime website What is ML.NET?
