Table Question-Answering with TAPAS in Python

Table question-answering (QA) is like asking Excel a natural language question and getting a truly intelligent, human-like response. We can ask something like "what is the total GDP across both China and Indonesia?" and Google's TAPAS (the machine learning model) will look at the table, find the two parts of the table needed to answer the question, sum both and return them. We learn how to apply TAPAS for table question answering using Hugging Face transformers and Python. We take this further by using a Pinecone vector database with a Microsoft MPNet Table question-answering (QA) model. With this, we can ask the question, search through a million, 10 million, or even a billion tables - retrieve the most relevant tables - and then answer the specific question again with Google's TAPAS. 🌲 Pinecone example: https://github.com/pinecone-io/examples/blob/master/learn/search/question-answering/table-qa.ipynb 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 🎉 Subscribe for Article and Video Updates! https://jamescalam.medium.com/subscribe https://medium.com/@jamescalam/membership 👾 Discord: https://discord.gg/c5QtDB9RAP 00:00 Intro 01:04 Table QA process 03:38 Getting the code 04:08 Colab GPU and prerequisites 04:33 Dataset download and preprocessing 06:10 Table QA retrieval pipeline 11:29 First test, can it retrieve tables? 12:55 TAPAS model for table QA 15:04 Asking more table QA questions 17:37 Asking advanced aggregation questions to TAPAS 19:38 Final thoughts