From 6b0f413eabfba8551fc4ee7e6d14afacd795217b Mon Sep 17 00:00:00 2001 From: mm Date: Fri, 5 May 2023 00:39:40 +0000 Subject: [PATCH] but why section --- README.md | 34 ++++++++++++++++++++++++++++++++++ 1 file changed, 34 insertions(+) diff --git a/README.md b/README.md index aaffb52..13d37d0 100644 --- a/README.md +++ b/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