TAPAS provides end-to-end neural models for question answering over tabular data, targeting researchers and practitioners in NLP and data analysis. It enables accurate extraction of information from tables by leveraging transformer architectures, offering improved performance on complex table-based queries.

How It Works

TAPAS utilizes a transformer-based architecture specifically designed to process tabular data alongside natural language questions. It encodes both the table and the question, allowing the model to learn relationships between them. Key innovations include specialized attention mechanisms that consider cell positions and table structure, enabling more effective reasoning over structured data.

Quick Start & Requirements

• Install: pip install -e . after cloning the repository.
• Prerequisites: protobuf-compiler (installable via sudo apt-get install protobuf-compiler on Ubuntu/Debian).
• Colab: A Colab notebook is available for trying predictions: https://colab.research.google.com/github/google-research/tapas/blob/master/notebooks/tapas_qa_example.ipynb
• Hugging Face: TAPAS is integrated into huggingface/transformers (v4.1.1+), offering 28 pre-trained checkpoints and a custom QA widget.

Highlighted Details

• Offers multiple pre-trained models (sizes: TINY to LARGE) for various tasks including Question Answering (WTQ, SQA, WikiSQL) and Table Entailment (TabFact).
• Supports an option reset_position_index_per_cell for potentially improved training.
• Includes code for intermediate pre-training and open-domain QA over tables via dense retrieval.
• Provides detailed instructions for pre-training and fine-tuning, including TensorFlow example creation using Google Dataflow.

Maintenance & Community

• The project is from Google Research.
• For help or issues, submit a GitHub issue.

Licensing & Compatibility

• The repository is not explicitly licensed in the README. However, as a Google Research project, it's likely intended for research purposes. Compatibility with commercial or closed-source applications is not specified.

Limitations & Caveats

• Training large models requires TPUs; GPU training is possible with reduced sequence length or batch size, potentially impacting accuracy.
• Evaluation metrics for WikiSQL may deviate from official scripts.
• The project is described as "not an official Google product."