Welcome to the world of Language Models and Text Analytics with GPT models and Azure OpenAI services!
Language Models are becoming increasingly popular for solving different text analytics tasks like summarization, question-answering, sentiment analysis, text generation, and more. These models are capable of learning and understanding the natural language in a way that they can generate coherent and relevant responses to complex queries. One such model is the GPT (Generative Pre-trained Transformer) model.
GPT models are a class of Language Models developed by OpenAI that are pre-trained on large amounts of text data, using unsupervised learning techniques. These models can be fine-tuned on specific tasks to produce state-of-the-art results on a variety of natural language processing tasks.
Azure OpenAI Services provide a powerful and easy-to-use platform for developers and researchers to experiment with and utilize these advanced NLP capabilities. With Azure OpenAI Services, you can easily integrate GPT models into your applications and use them to solve a wide range of text analytics problems.
One of the key techniques for utilizing GPT models effectively is prompt engineering. Prompt engineering is the process of designing high-quality prompts or templates that are used to guide the model to generate relevant and coherent responses. By carefully designing prompts, you can achieve better results on various NLP tasks like question-answering, summarization, and more.
In this repository, we will explore different text analytics tasks that can be performed using GPT models and Azure OpenAI Services. We will also discuss prompt engineering techniques. The repository contains code examples that use with Language Models to perform Text Analytics using open-source large language models and Azure OpenAI Services.