NLP pitstop using GPT-3.5 translate, summarize, sentiment analysis and Named Entity Extraction!!!

Introduction:

   Large Language Models (LLMs) represent a groundbreaking advancement in the field of artificial intelligence, particularly in natural language processing. These models are designed to understand, generate, and manipulate human-like text on a massive scale. One of the most notable examples of LLMs is OpenAI's GPT-3 (Generative Pre-trained Transformer 3), which is part of the Transformer architecture. What sets LLMs apart is their ability to learn from vast amounts of diverse data, enabling them to perform a wide range of language-related tasks, such as text completion, translation, summarization, and even creative writing. These models are pre-trained on extensive datasets and can then be fine-tuned for specific applications. The architecture of LLMs, particularly the Transformer model, utilizes attention mechanisms to capture relationships between words and phrases in a text. This allows the models to grasp context and generate coherent and contextually relevant responses. GPT-3, for instance, has a staggering number of parameters, reaching hundreds of billions, contributing to its ability to understand and generate complex language patterns. As researchers continue to explore and improve upon the capabilities of Large Language Models, the technology is expected to play a pivotal role in shaping the future of human-computer interaction, information retrieval, and content generation. However, it is crucial to approach the development and deployment of LLMs with a careful consideration of ethical implications and societal impact.

NLP pitstop powered by GenAI [gpt-3.5-turbo]:

    

Based on, Gadget framework [ A serverless Node.js environment for running your JavaScript code accompanied by Actions and HTTP routes that are used to execute and organize your backend code]. Above it has defined three data models: user, session and translation. Below is an example how it describes relation between variable. Ex: User has many translations. You may supply multiple languages separated by space or comma.

As user logs in after being authenticated into the application, it stores the records as below.

Below is the JS and is calling Open-AI API end point [openai.chat.completions]:

Access Gadget based NLP pitstop:

To access, you can use GMail to authenticate and login: translate and capture sentiment here

Hope you had fun using this simple translator with sentiment capture!!