Badhon/BanglaPunctDataset

Bangla Punctuation Restoration Dataset

A merged, high-quality Bangla dataset for punctuation restoration, formatted as instruction-tuning conversation pairs. The dataset is suitable for fine-tuning Large Language Models (LLMs) and sequence models to restore punctuation in Bangla text.

Dataset Summary

• —Language: Bengali (Bangla)
• —Task: Punctuation Restoration
• —Format: JSONL (instruction-style conversations)
• —Max chunk length: ~256 characters
• —Punctuation covered: । ! ? , ; : -

Each example contains:

• —An unpunctuated Bangla text as input
• —A punctuated Bangla text as output

Data Format

Each dataset entry follows the structure below:

{
  "conversations": [
    {
      "from": "human",
      "value": "এটি একটি উদাহরণ বাক্য যেখানে কোন বিরামচিহ্ন নেই"
    },
    {
      "from": "gpt",
      "value": "এটি একটি উদাহরণ বাক্য, যেখানে কোনো বিরামচিহ্ন নেই।"
    }
  ],
  "source": "source_name",
  "score": 7.83
}

Field Description

Data Sources

This dataset is created by merging and processing three main sources:

1. Badhon/BanglaQuranPunctuationDataset (Hugging Face)

• —Bangla Quran text with accurate punctuation
• —High grammatical and punctuation consistency

2. hishab/hishab-pr-bn-v1 (Hugging Face)

• —Bangla punctuation restoration dataset
• —Diverse sentence structures and punctuation styles

3. Web-Collected Bangla Text (Custom Scraper)

High-quality Bangla paragraphs collected from:

• —Bengali Wikipedia
• —Prothom Alo (prothomalo.com)
• —BBC Bangla (bbc.com/bengali)
• —Bangla blogs:

• —somewhereinblog.net
• —sachalayatan.com
• —amarblog.com

Data Processing

The following preprocessing steps were applied:

Cleaning

• —Preserved Bangla punctuation (। ! ? , ; : -)
• —Removed encoding artifacts and noisy symbols
• —Ensured Bangla language dominance

Chunking

• —Text split into chunks of ≤256 characters
• —Sentence-boundary–aware splitting
• —Avoided mid-sentence truncation

Quality Filtering

• —Minimum and maximum length thresholds
• —Required presence of natural punctuation
• —Removed malformed or low-quality samples

Deduplication

• —Removed duplicates using punctuated-text matching
• —Improved diversity and reduced redundancy

Intended Uses

• —Fine-tuning LLMs for Bangla punctuation restoration
• —Training instruction-following Bangla NLP models
• —Sequence-to-sequence or token-classification approaches
• —ASR post-processing pipelines (speech → text → punctuation)