@ -9,6 +9,40 @@ The project can be extended to include other distance metrics or additional data
> Note that this model only considers geographic distances and does not take into account other factors such as political borders or transportation infrastructure.
These factors contribute to a sense of "distance as it pertains to travel difficulty," which is not directly reflected by this model.
## But Why?
### Demonstrate Flexibility
The primary goal of this project is to demonstrate that pre-trained language models can be fine-tuned to understand geographic relationships between cities.
### Contribute to the Community
Out-of-the-box neural network models do not inherently understand how city names relate to one another in the same intuitive way our brains do.
This is because these models lack the necessary training and contextual information to grasp the spatial relationships, cultural connections, and other underlying patterns that humans can easily discern.
Training a specialized model can help bridge this gap, enabling the network to capture these complex relationships and better understand the nuances of geographic data.
By training a model on pairs of city names and their geodesic distances, we can improve its ability to infer the similarity between cities based on their names alone.
This can be beneficial in various applications, such as search engines, recommendation systems, or other natural language processing tasks that involve understanding the geographic context.
This model will be published and available for you to use, and the code can be adapted for other specialized use-cases.
### Explore Tradeoffs
This approach offers the advantage of understanding geographic relationships through language patterns alone, without relying on traditional methods like latitude/longitude lookups, enabling more efficient and context-aware processing of location-based information in natural language tasks.
Using a neural network for understanding geographic relationships provides a more robust and flexible representation of location-based information compared to traditional methods like latitude/longitude lookups.
This approach can capture complex patterns and relationships in natural language tasks that may not be easily modeled using traditional methods.
While there might be an initial computational overhead in training the model, the benefits include the ability to handle a variety of location-based queries (e.g., based on city landmarks), and better handling of aliases and alternate city names.
This tradeoff enables more efficient and context-aware processing of location-based information, making it a valuable choice in _certain_ applications.
In scenarios where high precision is required or where the problem can be solved quickly using traditional methods, table lookups and geodesic calculations might still be more suitable.
Ultimately, whether a neural network is more efficient than traditional methods depends on the specific problem being addressed and the desired trade-offs between accuracy, efficiency, and flexibility.
### Applicability to Other Tasks
Furthermore, the approach demonstrated in this project can be extended to other metrics or features beyond geographic distance.
By adapting the dataset generation and fine-tuning processes, you can train models to learn various relationships and similarities between different entities.
