Table Question Answering (Table QA) is the answering a question about an information on a given table. You can use the Table Question Answering models to simulate SQL execution by inputting a table.
In this tutorial we demonstrate how to use the base example. with OpenVINO. This example based on TAPAS base model fine-tuned on WikiTable Questions (WTQ) that is based on the paper TAPAS: Weakly Supervised Table Parsing via Pre-training.
Answering natural language questions over tables is usually seen as a semantic parsing task. To alleviate the collection cost of full logical forms, one popular approach focuses on weak supervision consisting of denotations instead of logical forms. However, training semantic parsers from weak supervision poses difficulties, and in addition, the generated logical forms are only used as an intermediate step prior to retrieving the denotation. In this paper, it is presented TAPAS, an approach to question answering over tables without generating logical forms. TAPAS trains from weak supervision, and predicts the denotation by selecting table cells and optionally applying a corresponding aggregation operator to such selection. TAPAS extends BERT's architecture to encode tables as input, initializes from an effective joint pre-training of text segments and tables crawled from Wikipedia, and is trained end-to-end.
The tutorial consists from following steps:
- Prerequisites
- Use the original model to run an inference
- Convert the original model to OpenVINO Intermediate Representation (IR) format
- Run the OpenVINO model
- Interactive inference
This is a self-contained example that relies solely on its own code.
We recommend running the notebook in a virtual environment. You only need a Jupyter server to start.
For details, please refer to Installation Guide.