Datasets:
Tasks:
Question Answering
Formats:
parquet
Sub-tasks:
extractive-qa
Languages:
Indonesian
Size:
1K - 10K
File size: 5,329 Bytes
846e346 56adf6e f4e13ad 846e346 c001d94 846e346 c001d94 846e346 48779b5 846e346 48779b5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 | ---
dataset_info:
features:
- name: id
dtype: string
- name: question_type
sequence: string
- name: question
dtype: string
- name: answer_coordinates
sequence:
sequence: int64
- name: answer_text
sequence: string
- name: table_source
dtype: string
- name: table
struct:
- name: data
sequence:
sequence: string
- name: header
sequence: string
- name: id
dtype: string
- name: title
dtype: string
splits:
- name: train
num_bytes: 4571025
num_examples: 2057
- name: test
num_bytes: 1075985
num_examples: 502
download_size: 1367492
dataset_size: 5647010
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
language:
- id
task_categories:
- question-answering
task_ids:
- extractive-qa
tags:
- table-question-answering
- tabular
- indonesian
- indohitab
---
# Dataset Card for IndoHiTab (Flattened Version)
## Dataset Description
**IndoHiTab** is a newly constructed, high-quality dataset specifically designed to solve the Table Question Answering (TQA) task for the Indonesian language. Due to the lack of publicly available resources in this domain, this dataset serves as the primary benchmark for evaluating extractive table parsers, such as the IndoTaPas model.
This specific dataset repository contains the **Flattened Version** of the **primary, manually annotated** IndoHiTab dataset. It was developed by translating a subset of the English HiTab dataset using human annotators under a standardized protocol. This is the exact preprocessed format utilized in our study to train and evaluate the TaPas architecture.
<!-- - **Curated by:** Muhammad Rizki Syazali & Evi Yulianti
- **Language(s) (NLP):** Indonesian (`id`) -->
## Dataset Structure
### Dataset Statistics
After filtering out questions requiring header selection (due to TaPas architectural constraints), this manually annotated dataset contains a total of **2,559 instances**, split into training and testing sets:
| Dataset | Train | Test | Translation Method | Purpose |
| :--- | :---: | :---: | :--- | :--- |
| **IndoHiTab** | 2,057 | 502 | Manual (Human) | Main training and evaluation |
### Data Instances (Flattened Structure)
In this **Flattened Format**, tables with multi-level (hierarchical) column and row headers have been preprocessed and converted into a flat, 1-dimensional structure. This is achieved by concatenating the top-level header with lower-level headers. The resulting table includes a dedicated `header` array, and all string values (including the data matrix) are lowercased.
**Example Instance:**
```json
{
"id": "00d47c9aac5050539645dcae34f78570",
"question_type": ["none"],
"question": "berapa persen penduduk canada dengan pendapatan rumah tangga sebesar $150,000 atau lebih yang mengatakan bahwa pengaruh hoki terhadap identitas nasional sangat penting?",
"answer_coordinates": [[21, 5]],
"answer_text": ["49"],
"table_source": "statcan",
"table": {
"data": [
["total", "70", "69", "64", "55", "46"],
["kelompok umur", "", "", "", "", ""],
["kelompok umur 15-24", "70", "56", "51", "48", "47"],
["kelompok umur 25-34", "71", "65", "59", "51", "50"],
["tingkat pendidikan tertinggi yang ditamatkan di bawah sekolah menengah", "65", "68", "66", "60", "46"],
["..."]
],
"header": [
"karakteristik sosio-demografi dan ekonomi",
"piagam hak dan kebebasan persen",
"bendera persen",
"lagu kebangsaan persen",
"rcmp persen",
"hoki persen"
],
"id": "2613",
"title": "persepsi simbol nasional sebagai hal yang sangat penting untuk identitas kanada, berdasarkan karakteristik sosio-demografi dan ekonomi, 2013"
}
}
```
### Data Fields
* **`id`**: Unique string identifier for the question-table pair.
* **`question_type`**: List of strings indicating the type of question.
* **`question`**: The natural language question in Indonesian.
* **`answer_coordinates`**: List of lists containing the `[row_index, column_index]` of the exact answer(s) within the flattened data matrix.
* **`answer_text`**: List of strings containing the expected answer value(s).
* **`table_source`**: Source of the original table (e.g., `statcan`, `totto`, `nsf`).
* **`table`**: A dictionary representing the flattened table containing:
* **`header`**: A 1-dimensional array of column headers.
* **`data`**: A 2-dimensional array (matrix) of the table's row contents.
* **`id`**: The table identifier.
* **`title`**: The title or caption of the table.
## How to Use
You can easily load this dataset using the `datasets` library from Hugging Face:
```python
from datasets import load_dataset
# Load the dataset
dataset = load_dataset("rizki-syazali/itqa-flatten-header")
# Print the first instance of the training set
print(dataset['train'][0])
```
<!-- ## Citation Information
If you use the IndoHiTab dataset in your research, please consider citing our paper:
**BibTeX:**
```bibtex
@article{syazali2026indotapas,
title={IndoTaPas: A TaPas-Based Model for Indonesian Table Question Answering},
author={Syazali, Rizki and Co-authors},
journal={Expert Systems with Applications},
year={2026}
}
``` -->
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