Learn how to implement table question-answering using TAPAS in Python. Explore the process of asking natural language questions to tables and receiving intelligent, human-like responses. Discover how to apply TAPAS for table QA using Hugging Face transformers and Python. Dive into advanced techniques by integrating Pinecone vector database with a Microsoft MPNet Table QA model to search through vast numbers of tables and retrieve relevant information. Follow along with code examples, dataset preprocessing, and the creation of a table QA retrieval pipeline. Test the model's ability to retrieve tables, ask various questions, and handle advanced aggregation queries. Gain insights into the practical applications of table QA and its potential for analyzing large-scale tabular data.
Overview
Syllabus
Intro
Table QA process
Getting the code
Colab GPU and prerequisites
Dataset download and preprocessing
Table QA retrieval pipeline
First test, can it retrieve tables?
TAPAS model for table QA
Asking more table QA questions
Asking advanced aggregation questions to TAPAS
Final thoughts
Taught by
James Briggs
