📊 Tacha: Table Chunking Assessment

Financial Tables for Evaluating Chunking Algorithms

Tacha is a dataset derived from TAT-QA, containing financial documents with tables, designed to evaluate how well chunking algorithms handle structured tabular data mixed with narrative text.

Key Challenges

This dataset tests chunking algorithms on:

• Tabular data structures
• Numerical reasoning across rows/columns
• Table headers and cell relationships
• Mixed table and text content
• Financial calculations and comparisons
• Cross-references between tables and narrative

Dataset Structure

Corpus Config

Field	Description
id	Unique document identifier
text	Full document with tables

Questions Config

Field	Description
question	Question about the document/table
answer	Answer (may include calculations)
chunk-must-contain	Text/table passage that must be in the retrieved chunk
document_id	Reference to corpus document

Usage

from datasets import load_dataset

# Load corpus
corpus = load_dataset("chonkie-ai/tacha", "corpus", split="train")

# Load questions
questions = load_dataset("chonkie-ai/tacha", "questions", split="train")

Part of MTCB

Tacha is part of the Massive Text Chunking Benchmark (MTCB), a comprehensive benchmark for evaluating RAG chunking strategies.

Citation

If you use this dataset, please also cite the original TAT-QA paper.

License

CC-BY-4.0