Bengali Summarizer Mt5

🚀 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.

📦 Installation

No specific installation steps are provided in the original document.

💻 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))

📚 Documentation

Model Details

Model Information

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

⚠️ 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

💡 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.

📄 License

This model is released under the MIT License.

📚 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}}
}