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README.md

@@ -11,29 +11,27 @@ These factors contribute to a sense of "distance as it pertains to travel diffic
 
 ## But Why?
 
-The primary goal of this project is to demonstrate that pre-trained language models can be fine-tuned to understand geographic relationships between cities.
+### Demonstrate Flexibility
+This project showcases how pre-trained language models can be fine-tuned to understand geographic relationships between cities.
 
-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.
+### Contribute to the Community
-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.
+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.
 
-### Tradeoffs
+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 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 model will be published for public use, and the code can be adapted for other specialized use-cases.
-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.
+### 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.
 
-This tradeoff enables more efficient and context-aware processing of location-based information, making it a valuable choice in _certain_ applications.
+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.
 
-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.
+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, 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.
+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
-Furthermore, the approach demonstrated in this project can be extended to other metrics or features beyond geographic distance.
+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.
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-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