BanglaT5
| Property | Value |
|---|---|
| Parameter Count | 247M |
| Model Type | Sequence-to-Sequence Transformer |
| Training Objective | Span Corruption |
| Paper | BanglaNLG Paper |
| Author | csebuetnlp |
What is BanglaT5?
BanglaT5 is a state-of-the-art sequence-to-sequence transformer model specifically designed for Bengali natural language processing tasks. With 247M parameters, it represents a significant advancement in Bengali language technology, outperforming larger models like mT5-base (582M parameters) across various NLP tasks.
Implementation Details
The model is pre-trained using a "Span Corruption" objective and requires specific normalization pipeline for optimal performance. It's implemented using the Hugging Face transformers library and includes custom text normalization tools for preprocessing Bengali text.
- Custom normalization pipeline required for optimal performance
- Achieves SOTA results with significantly fewer parameters than competitors
- Comprehensive evaluation across 6 different NLP tasks
Core Capabilities
- Machine Translation (38.8/25.2 SacreBLEU)
- Abstractive Text Summarization (13.7 ROUGE-2)
- Question Answering (68.5/74.8 EM/F1)
- Multi-turn Dialogue Generation (19.0 SacreBLEU-1)
- News Headline Generation (13.8 ROUGE-2)
- Cross-lingual Summarization (6.4/4.0 ROUGE-2)
Frequently Asked Questions
Q: What makes this model unique?
BanglaT5 achieves state-of-the-art performance on Bengali NLP tasks while using significantly fewer parameters than multilingual alternatives. It demonstrates superior performance across all benchmarked tasks, achieving a BNLG score of 29.4, compared to mT5's 24.9.
Q: What are the recommended use cases?
The model excels in various NLP tasks including machine translation, text summarization, question answering, dialogue generation, and news headline generation. It's particularly effective for applications requiring Bengali language understanding and generation.
