less wordy

README.md

@@ -9,6 +9,30 @@ 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
+This project showcases how 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 struggle to grasp the spatial relationships, cultural connections, and underlying patterns between cities that humans intuitively understand. Training a specialized model bridges this gap, allowing the network to capture complex relationships and better comprehend geographic data.
+
+By training a model on pairs of city names and geodesic distances, we enhance its ability to infer city similarity based on names alone. This is beneficial in applications like search engines, recommendation systems, or other natural language processing tasks involving geographic context.
+
+This model will be published for public use, and the code can be adapted for other specialized use-cases.
+
+### Explore Tradeoffs
+Using a neural network to understand geographic relationships provides a more robust and flexible representation compared to traditional latitude/longitude lookups. It can capture complex patterns and relationships in natural language tasks that may be difficult to model using traditional methods.
+
+Although there's an initial computational overhead in training the model, benefits include handling various location-based queries and better handling of aliases and alternate city names.
+
+This tradeoff allows more efficient and context-aware processing of location-based information, making it valuable in specific applications. In scenarios requiring high precision or quick solutions, traditional methods may still be more suitable.
+
+Ultimately, the efficiency of a neural network compared to traditional methods depends on the specific problem and desired trade-offs between accuracy, efficiency, and flexibility.
+
+### Applicability to Other Tasks
+The approach demonstrated can be extended to other metrics or features beyond geographic distance. By adapting dataset generation and fine-tuning processes, models can be trained to learn various relationships and similarities between different entities.
+
 
 ## Overview of Project Files