Create README.md

README.md

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+---
+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
+---
+
+
+# Aminul PR Bengali (Adversarial Attack)
+
+**Dataset Name:** `ha-pr-bn-aminul-attack`  
+**Language:** Bengali (bn)  
+**Task:** Punctuation Restoration (Text-to-Text Generation)  
+**Size:** 11,113 conversations  
+**License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)  
+**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