awebgis-mini

Model Description

awebgis-mini is a fine-tuned T5-efficient-mini model developed for Autonomous Web-based Geographical Information Systems (AWebGIS). This model provides a balanced approach between model size and performance, offering improved accuracy over the tiny variant while remaining efficient for web deployment. It excels at converting natural language queries into structured geospatial function calls, enabling seamless interaction with GIS applications.

The model is designed to operate autonomously in web environments, processing geospatial queries locally without requiring cloud-based inference, which enhances user privacy and reduces response times.

Model Details

• Base Model: google/t5-efficient-mini
• Fine-tuned for: Natural language to geospatial function call conversion
• Task: Sequence-to-sequence text generation
• Language: English
• Training Framework: PyTorch with Hugging Face Transformers

Model Specifications

• Number of Parameters: ~29 million
• Floating Point Precision: FP32 (32-bit)
• Approximate Model Size: ~116 MB

Model Variants

This model is part of a family of fine-tuned models for AWebGIS:

• awebgis-tiny - Lightweight variant for resource-constrained environments
• awebgis-mini (this model) - Balanced variant with improved accuracy
• awebgis-small - Higher capacity variant for complex queries

How to Use

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("mahdin75/awebgis-mini")
model = AutoModelForSeq2SeqLM.from_pretrained("mahdin75/awebgis-mini")

# Example usage
input_text = "Show me all parks near the city center and calculate their total area"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=128)
result = tokenizer.decode(outputs[0], skip_special_tokens=True)

Fine-Tuning

To fine-tune this model for your specific geospatial tasks, refer to the training documentation in the main repository:

1. Training Information: Check the GitHub repository for training scripts and documentation
2. Data Format: Prepare your dataset with natural language inputs and corresponding geospatial function call outputs
3. Training Setup: Follow the instructions in the repository to set up the Python environment and dependencies
4. Hyperparameters: Adjust learning rate, batch size, and training epochs based on your dataset size and requirements

For detailed fine-tuning instructions and training code, visit the main repository.

Live Demo

You can interact with this model through the web application:

🔗 AWebGIS Application - React-based frontend that demonstrates the model's capabilities in a real-world GIS interface

Citation

If you use this model in your research or applications, please cite the following paper:

@misc{ashani2025finetuningsmalllanguagemodels,
      title={Fine-Tuning Small Language Models (SLMs) for Autonomous Web-based Geographical Information Systems (AWebGIS)},
      author={Mahdi Nazari Ashani and Ali Asghar Alesheikh and Saba Kazemi and Kimya Kheirkhah and Yasin Mohammadi and Fatemeh Rezaie and Amir Mahdi Manafi and Hedieh Zarkesh},
      year={2025},
      eprint={2508.04846},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2508.04846},
}

Resources

• 📄 Preprint: arXiv:2508.04846
• 💻 GitHub Repository: github.com/mahdin75/awebgis
• 🌐 Web Application: github.com/mahdin75/awebgis-app

License

This model is released under the MIT License. See the LICENSE file for more details.

Important: This model is fine-tuned from google/t5-efficient-mini, which is licensed under the Apache License 2.0. The LICENSE file includes both licenses and proper attribution. Users must comply with both license terms when using this model.