bengali-t5-base
| Property | Value |
|---|---|
| Training Data | ~11B tokens |
| Base Architecture | T5-base |
| Development | Flax/Jax Community Week |
| Model Hub | Hugging Face |
What is bengali-t5-base?
bengali-t5-base is a specialized language model trained on the Bengali portion of the MT5 dataset, developed during the Flax/Jax Community Week with support from Google's TPU program. The model leverages the T5-base architecture and has been trained on approximately 11 billion tokens using specific batch parameters (64 size batch, 512 tokens) over 350,000 training steps.
Implementation Details
The model is implemented using the Hugging Face transformers library and can be easily loaded using both the tokenizer and model classes. It utilizes a denoising objective training approach but currently lacks direct generation capabilities without additional fine-tuning.
- Built on T5-base architecture
- Trained using Flax/Jax framework
- Implements custom tokenization for Bengali text
- Requires fine-tuning for generation tasks
Core Capabilities
- Bengali text tokenization and processing
- Support for basic text encoding and decoding
- Foundation for downstream Bengali language tasks
- Extensible for custom fine-tuning
Frequently Asked Questions
Q: What makes this model unique?
This model is specifically designed for Bengali language processing, trained on a massive dataset of 11B tokens, making it one of the significant Bengali language models available. Its T5-base architecture provides a strong foundation for various NLP tasks.
Q: What are the recommended use cases?
The model is best suited for Bengali text processing tasks after appropriate fine-tuning. While it doesn't have built-in generation capabilities, it can be fine-tuned for specific downstream tasks using the prefix-LM objective mentioned in the documentation.
