JSON (JavaScript Object Notation) is a widely used data format for data exchanges between services, servers, and organizations. The JSON is easy to read, and easy to parse, making it the ideal choice for data exchange. However, if you’re working on a project which requires a large amount of sample JSON data to test the functionality of your business logic or to prototype your user interface it is not an easy task to write sample JSON data. To address this issue JSONStudio provides a free solution to generate sample JSON data with a click of a button.
JSONStudio is a free online tool that provides many services to generate, convert, visualize, modify, sort, and format JSON data. Using the new innovative tool of JSONStudio which is powered by Chat GPT to generate JSON data based on the input text given by the user.
Using these tools users can easily generate their desired sample JSON by giving a short description of the sample JSON data or specifying the structure of the JSON data
Chat GPT is an advanced deep-learning algorithm developed by OpenAI. JSONStudio utilizes this technology to generate sample JSON data based on user input.
Architecture of ChatGPT:
ChatGPT is developed using the transformer architecture that uses self-attention mechanisms to understand the context of given input text data. Vaswani et al a computer scientist he is the first to come up with the idea of transformer architecture published in the paper called "Attention is All You Need”. From this paper, many realized that we can achieve high performance in machine translation using self-attention mechanisms.In the development of ChatGPT, the same transformer architecture is used to generate response text messages for the given input. The model was fed with a sequence of tokens, representing the input data and is trained to predict the next token in the sequence of data. This process is repeated iteratively with the output of each iteration used as input for the next iteration. The model data training process was done on multiple GPUs, using the Adam optimizer and a learning rate schedule that gradually decreased over time. The training process took several weeks to required a huge amount of computational resources