---
license: cc-by-4.0
dataset_info:
- config_name: corpus
  features:
  - name: id
    dtype: string
  - name: text
    dtype: string
  - name: table_rows
    dtype: int64
  - name: table_cols
    dtype: int64
  - name: num_paragraphs
    dtype: int64
  splits:
  - name: train
    num_bytes: 1665939
    num_examples: 349
  download_size: 1665939
  dataset_size: 1665939
- config_name: questions
  features:
  - name: id
    dtype: string
  - name: doc_id
    dtype: string
  - name: question
    dtype: string
  - name: answer
    dtype: string
  - name: answer_type
    dtype: string
  - name: answer_from
    dtype: string
  - name: chunk-must-contain
    dtype: string
  splits:
  - name: train
    num_bytes: 1939261
    num_examples: 2065
  download_size: 1939261
  dataset_size: 1939261
configs:
- config_name: corpus
  data_files:
  - split: train
    path: corpus/train-*
- config_name: questions
  data_files:
  - split: train
    path: questions/train-*
---

<div align="center">

# 📊 Tacha: Table Chunking Assessment

*Financial Tables for Evaluating Chunking Algorithms*

</div>

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.

## Dataset Description

- **Documents**: 349 financial documents with tables
- **Questions**: 2,065 question-answer pairs
- **Domain**: Financial Tables
- **Source**: TAT-QA dataset

## 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

```python
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)](https://github.com/chonkie-inc/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