google-research/tapas

Health v2 · maintenance only

Flags: no_releases archived

Adoption not part of the score

1202 stars · 214 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

TAPAS is Google Research's implementation of transformer-based models for question answering over tables, including pretrained checkpoints and training code. It also covers related models for table retrieval, open-domain QA over tables, table entailment (TabFact), and efficient table attention variants (MATE, DoT).

Use cases

• answer natural language questions against tabular data
• parse tables with a neural question answering model
• retrieve relevant tables for open-domain question answering
• classify whether a statement is entailed by a table (TabFact)
• fine-tune a table QA model on a custom dataset
• run efficient transformer attention over large tables

When to choose

• you need state-of-the-art neural table question answering with pretrained checkpoints
• you are doing research on table-text understanding or table retrieval
• you want TensorFlow-based training code for table parsing models

When to avoid

• you need a production-ready QA service rather than research code
• you prefer PyTorch or want to use the models via Hugging Face Transformers instead
• you need general document or text QA not grounded in tables

Facets

library · maturity maintenance

nlp machine-learning deep-learning search-engine machine-learning deep-learning python table-question-answering table-parsing transformers tensorflow research-code question-answering table-retrieval natural-language-processing search linux gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="google-research/tapas")