but why section

README.md

@@ -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.
+
 
 ## Overview of Project Files