| --- |
| 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 |
|
|
