đ Bengali Summarizer MT5
This model is a fine - tuned version of the MT5 model, specifically designed for text summarization in Bengali. It can generate concise summaries for Bengali text, useful in various applications like news summarization.
⨠Features
- Tailored for Bengali: Specialized in generating summaries for Bengali text.
- Based on MT5: Fine - tuned from the [MT5](https://huggingface.co/google/mt5 - base) model.
đĻ Installation
No specific installation steps are provided in the original document.
đģ Usage Examples
Basic Usage
from transformers import MT5ForConditionalGeneration, MT5Tokenizer
model_name = "tashfiq61/bengali - summarizer - mt5"
tokenizer = MT5Tokenizer.from_pretrained(model_name)
model = MT5ForConditionalGeneration.from_pretrained(model_name)
def summarize(text):
inputs = tokenizer.encode("summarize: " + text, return_tensors="pt", max_length=512, truncation=True)
summary_ids = model.generate(inputs, max_length=150, min_length=40, length_penalty=2.0, num_beams=4, early_stopping=True)
return tokenizer.decode(summary_ids[0], skip_special_tokens=True)
text = "Your Bengali text here."
print(summarize(text))
đ Documentation
Model Details
Property |
Details |
Developed by |
Tashfiqul Islam, Tashin Mahmud Khan, Amir Hamja Marjan, Simul Hossain |
Model Type |
Bengali Text Summarization |
Language |
Bengali (bn ) |
License |
MIT License |
Fine - tuned from |
[google/mt5 - base](https://huggingface.co/google/mt5 - base) |
Model Information
Property |
Details |
Website Link |
[BTS Website](https://bengali - text - summarizer - website.vercel.app/) |
Repository Link |
[Github Repo](https://github.com/tashfiqul - islam/bengali - text - summarizer - website) |
Uses
Direct Use
This model is intended for generating concise summaries of Bengali text inputs, making it useful for applications like news summarization, content aggregation, and more.
Downstream Use
Users can integrate this model into larger systems requiring text summarization capabilities in Bengali.
Out - of - Scope Use
The model is not designed for tasks outside text summarization, such as translation or sentiment analysis.
Bias, Risks, and Limitations
â ī¸ Important Note
While the model performs well on the training data, it may not generalize perfectly to all Bengali text. Users should be cautious of potential biases present in the training data and avoid using the model for critical applications without thorough evaluation.
Recommendations
đĄ Usage Tip
Users should evaluate the model's performance on their specific datasets and consider fine - tuning further if necessary. It's also recommended to monitor the model's outputs for any unintended biases or errors.
đ License
This model is released under the MIT License.
đ Citation
If you use this model, please cite:
@misc{islam2024bengalisummarizer,
title={Bengali Summarizer MT5},
author={Tashfiqul Islam},
year={2024},
publisher={Hugging Face},
howpublished={\url{https://huggingface.co/tashfiq61/bengali-summarizer-mt5}}
}