BanglaPunctDataset / README.md
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---
license: apache-2.0
task_categories:
- token-classification
- text-classification
tags:
- code
---
# 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:
```json
{
"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
| Field | Description |
| --------------- | -------------------------------------------------- |
| `conversations` | Instruction-style input–output pair |
| `source` | Origin of the sample |
| `score` | Random float (4.0–10.0), placeholder quality score |
---
## 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)