---
task_categories:
- text2text-generation
language:
- bn
tags:
- bangla
- punctuation-restoration
- adversarial-examples
- seq2seq
- nlp
pretty_name: Bangla Punctuation Restoration (Adversarial Attack)
size_categories:
- 10K<n<100K
---


# ha-pr-bn-aminul-attack

**Dataset Name:** `ha-pr-bn-aminul-attack`  
**Language:** Bengali (bn)  
**Task:** Punctuation Restoration (Text-to-Text Generation)  
**Size:** 11,113 conversations   
**Format:** Human-assistant conversations with adversarially perturbed inputs



## Dataset Description

This dataset extends `ha-pr-bn-aminul-generated` with **adversarial attacks** on the unpunctuated inputs. It simulates noisy or corrupted Bangla text, often produced by OCR, ASR, or user typos. The assistant still returns the original, correct punctuated version.



## Dataset Summary

- **Language:** Bengali (bn)  
- **Task:** Punctuation Restoration (Text-to-Text Generation)  
- **Size:** 11,113 adversarial examples  
- **License:** CC BY 4.0  
- **Purpose:** Test the robustness of punctuation models under noisy input conditions



## Dataset Structure

### Data Instances

```json
{
  "input": "শুধু বর্ষা নয় শীত বা গ্রীষ্মেও বেড়ে যাচ্ছে তাপ",
  "target": "শুধু বর্ষা নয়, শীত বা গ্রীষ্মেও বেড়ে যাচ্ছে তাপ।"
}
```
## Usage

```python
from datasets import load_dataset

dataset = load_dataset("aminul/ha-pr-bn-aminul-attack")
print(dataset["train"][0])
```

## Dataset Creation

### Attack Methods

Generated using `TextAttack` techniques:
- Word-level swaps
- Misspellings
- OCR-style distortions
- Transliteration and ASR-style noise

### Processing

1. Perturb the input sentence  
2. Preserve original `target` (punctuated) text