In this video, Lucidate demonstrates how to use APIs and audio to build prompts and completions for fine-tuning Transformers. Transformers are a type of machine learning model that have revolutionized the field of natural language processing. They use a deep learning architecture to generate human-like text and have been used to develop powerful language AI models such as ChatGPT.
Click '> Play' above to find out how to use APIs and audio to create specialized, bespoke AI NLP models'
The video begins with a discussion on prompt and completion datasets and their usefulness in training and fine-tuning language models to generate human-like text. We then demonstrate how to use the News API with Python to create a dataset of news articles that can be used to train and fine-tune models like GPT-3. This is done by importing necessary libraries, defining the News API endpoint and API key as variables, and making a request to the News API to retrieve the latest news articles. The article information is then parsed and stored in a Pandas dataframe in a structured format with the headline as the prompt and the article content as the completion.
The video then moves on to audio streams and how they can be used to build data pipelines for fine-tuning language models. Walker discusses OpenAI's Whisper, which is designed to transcribe spoken words into text with high accuracy. The video then shows how to extract the text from a YouTube video using Whisper and YouTube DL, and how to automate the workflow using Python classes and modules to build specialized language models in any field of choice.
After watching this video you will understand end-to-end how to use APIs and audio to fine-tune Transformers to create specialized AI language models.