---
license: cc-by-4.0
task_categories:
  - visual-question-answering
  - image-to-text
language:
  - bn
  - en
  - gu
  - hi
  - kn
  - ml
  - mr
  - ne
  - pa
  - ta
  - te
tags:
  - chart-qa
  - multilingual
  - indic
  - vqa
  - chart-understanding
size_categories:
  - 100K<n<1M
dataset_info:
  features:
    - name: image_id
      dtype: string
    - name: image
      dtype: image
    - name: chart_type
      dtype: string
    - name: language
      dtype: string
    - name: split
      dtype: string
    - name: question_ids
      sequence: int64
    - name: questions
      sequence: string
    - name: answers
      sequence: string
    - name: question_types
      sequence: string
  splits:
    - name: train
      num_examples: 352000
    - name: validation
      num_examples: 44000
    - name: test
      num_examples: 44000
---

# MLCQA v1

**M**ultilingual **L**anguage **C**hart **Q**uestion **A**nswering — A large-scale
multilingual Chart VQA dataset covering **11 languages** and **8 chart types**.

## Dataset Summary

| Metric | Count |
|---|---|
| **Total Images** | 440,000 |
| **Total QA Pairs** | 4,400,000 |
| **Languages** | 11 (bn, en, gu, hi, kn, ml, mr, ne, pa, ta, te) |
| **Chart Types** | 8 (area, donut, grouped_bar, horizontal_bar, line, pie, stacked_bar, vertical_bar) |
| **Questions per Image** | 10 |
| **Random Seed** | 22 |

### Splits

| Split | Images | QA Pairs |
|---|---|---|
| Train | 352,000 | 3,520,000 |
| Validation | 44,000 | 440,000 |
| Test | 44,000 | 440,000 |

## Data Format

The dataset is stored in Parquet format with **one row per image**. Each row
contains the chart image and a list of 10 question–answer pairs.

### Usage

```python
from datasets import load_dataset

ds = load_dataset("MLCQA/MLCQA-dataset")

# Access data
example = ds["train"][0]
print(example["image_id"])        # "train_en_bar_0101"
print(example["image"])           # PIL Image
print(example["questions"])       # list of 10 questions
print(example["answers"])         # list of 10 answers
print(example["chart_type"])      # "vertical_bar"
print(example["language"])        # "en"
```

### Schema

| Field | Type | Description |
|---|---|---|
| `image_id` | string | Unique image identifier (`{split}_{lang}_{chart}_{num}`) |
| `image` | image | Chart image (PNG embedded as bytes) |
| `chart_type` | string | Type of chart (area, donut, grouped_bar, etc.) |
| `language` | string | ISO 639-1 language code |
| `split` | string | Dataset split (train/validation/test) |
| `question_ids` | list[int] | List of 10 globally unique question IDs |
| `questions` | list[string] | List of 10 natural language questions |
| `answers` | list[string] | List of 10 ground-truth answers |
| `question_types` | list[string] | List of 10 question categories |