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xukehu/
Llama2-13B-LoRA-Toponym-Resolution

Name: Llama2-13B-LoRA-Toponym-Resolution
Availability: InStock

apache-2.0

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Llama2-13B-LoRA-Toponym-Resolution

This model includes the LoRA (Low-Rank Adaptation) weights fine-tuned for toponym resolution based on the Llama2 13B 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: Llama2 13B
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

You can load this model with its LoRA weights using the transformers and peft libraries as follows:

python

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

# Load the base model and tokenizer
model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-13b-chat-hf")
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-13b-chat-hf")

# Load LoRA weights for toponym resolution
model = PeftModel.from_pretrained(model, "xukehu/Llama2-13B-LoRA-Toponym-Resolution")

Citation

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

bibtex

@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}
}