# Tabula Rasa Documentation **Production Table Knowledge LLM: Teaching LLMs to accurately answer questions about tabular data** Tabula Rasa is a machine learning framework designed to help large language models (LLMs) accurately understand and answer questions about tabular data. Built on modern transformer architectures, it provides tools for training, evaluation, and deployment of table-aware language models. ## Features - 🎯 **Table-Aware LLMs**: Specialized models for understanding tabular data - 🚀 **Modern Architecture**: Built on PyTorch and Transformers - 📊 **Comprehensive Evaluation**: Tools for assessing model performance on table QA tasks - 🔧 **Easy Integration**: Simple API for training and inference - 📈 **Production Ready**: Optimized for deployment in production environments ## Quick Start ### Installation ```bash pip install tabula-rasa ``` For development installation: ```bash git clone https://github.com/gojiplus/tabula-rasa.git cd tabula-rasa pip install -e ".[dev]" ``` ### Basic Usage ```python from tabula_rasa import TabulaRasa # Initialize the model model = TabulaRasa() # Process tabular data table = { "columns": ["Name", "Age", "City"], "rows": [ ["Alice", 30, "New York"], ["Bob", 25, "San Francisco"], ] } # Ask a question question = "What is Alice's age?" answer = model.answer(question, table) print(answer) # Output: 30 ``` ## Contents ```{toctree} :maxdepth: 2 :caption: User Guide guides/installation guides/quickstart guides/training guides/evaluation ``` ```{toctree} :maxdepth: 2 :caption: API Reference api/modules ``` ```{toctree} :maxdepth: 1 :caption: Development GitHub Repository Contributing Changelog ``` ## Project Links - **GitHub**: [github.com/gojiplus/tabula-rasa](https://github.com/gojiplus/tabula-rasa) - **Issues**: [Issue Tracker](https://github.com/gojiplus/tabula-rasa/issues) - **License**: MIT ## Indices and Tables - {ref}`genindex` - {ref}`modindex` - {ref}`search`