Join our chill space, unwind, chat about Feminist AI and contribute to the PyData London DIY collage zine.

NLP and data science could be so easy if all of our data came as clean and plain text. But in practice, a lot of it is hidden away in PDFs, Word documents, scans and other formats that have been a nightmare to work with. In this talk, I'll present a new and modular approach for building robust document understanding systems, using state-of-the-art models and the awesome Python ecosystem. I'll show you how you can go from PDFs to structured data and even build fully custom information extraction pipelines for your specific use case.

In this talk, I'll show how large language models such as GPT-3 complement rather than replace existing machine learning workflows. Initial annotations are gathered from the OpenAI API via zero- or few-shot learning, and then corrected by a human decision maker using an annotation tool. The resulting annotations can then be used to train and evaluate models as normal. This process results in higher accuracy than can be achieved from the OpenAI API alone, with the added benefit that you'll own and control the model for runtime.