Falcon-7B-LoRA-Toponym-Resolution

Falcon-7B-LoRA-Toponym-Resolution

This model includes the LoRA (Low-Rank Adaptation) weights fine-tuned for toponym resolution based on the Falcon 7B architecture. It specializes in disambiguating geographic names (toponyms) to provide unambiguous references, often in the form of structured addresses (e.g., city, state, country). For example, given the toponym "Paris", the model may output "Paris, TX, US" based on the context. By further querying geocodes, such as Nominatim, GeoNames, or Google Maps API, the geo-coordinates and other properties, such as population, type can be determined.

Model Summary

• Base Model: Falcon 7B
• Fine-tuning Technique: LoRA (Low-Rank Adaptation)
• Task: Toponym Resolution
• Objective: To identify and resolve toponyms, providing specific addresses or references for geographic names within text.

Usage

This model is ideal for geoparsing and geographic information extraction tasks, especially in:

• Research papers and scientific literature
• Climate impact reports
• Social media and web data related to environmental topics

Loading the Model

Please refer to the lit_GPT project for instructions on how to use the fine-tuned model.

Citation

If you use this model, please cite the following publication:

@article{hu2024toponym,
  title={Toponym resolution leveraging lightweight and open-source large language models and geo-knowledge},
  author={Hu, Xuke and Kersten, Jens and Klan, Friederike and Farzana, Sheikh Mastura},
  journal={International Journal of Geographical Information Science},
  pages={1--28},
  year={2024},
  publisher={Taylor & Francis}
}