CityBert

CityBert is a machine learning project that fine-tunes a neural network model to understand the similarity between cities based on their geodesic distances.

The project generates a dataset of US cities and their pair-wise geodesic distances, which are then used to train the model.

The project can be extended to include other distance metrics or additional data, such as airport codes, city aliases, or time zones.

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

• generate_data.py: Generates a dataset of US cities and their pairwise geodesic distances.
• train.py: Trains the neural network model using the generated dataset.
• eval.py: Evaluates the trained model by comparing the similarity between city vectors before and after training.
• Makefile: Automates the execution of various tasks, such as generating data, training, and evaluation.
• README.md: Provides a description of the project, instructions on how to use it, and expected results.
• requirements.txt: Defines requirements used for creating the results.

How to Use

1. Install the required dependencies by running pip install -r requirements.txt.
2. Run make city_distances.csv to generate the dataset of city distances.
3. Run make train to train the neural network model.
4. Run make eval to evaluate the trained model and generate evaluation plots.

You can also just run make (i.e., make all) which will run through all of those steps.

What to Expect

After training, the model should be able to understand the similarity between cities based on their geodesic distances. You can inspect the evaluation plots generated by the eval.py script to see the improvement in similarity scores before and after training.

After five epochs, the model no longer treats the terms as unrelated:

After ten epochs, we can see the model has learned to correlate our desired quantities:

The above plots are examples showing the relationship between geodesic distance and the similarity between the embedded vectors (1 = more similar), for 10,000 randomly selected pairs of US cities (re-sampled for each image).

Note the (vertical) "gap" we see in the image, corresponding to the size of the continental United States (~5,000 km)

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Future Improvements

There are several potential improvements and extensions to the current model:

1. Incorporate airport codes: Train the model to understand the unique codes of airports, which could be useful for search engines and other applications.
2. Add city aliases: Enhance the dataset with city aliases, so the model can recognize different names for the same city. The geonamescache package already includes these.
3. Include time zones: Train the model to understand time zone differences between cities, which could be helpful for various time-sensitive use cases. The geonamescache package already includes this data, but how to calculate the hours between them is an open question.
4. Expand to other distance metrics: Adapt the model to consider other measures of distance, such as transportation infrastructure or travel time.
5. Train on sentences: Improve the model's performance on sentences by adding training and validation examples that involve city names in the context of sentences. Can use generative AI to create template sentences (mad-libs style) to create random and diverse training examples.
6. Global city support: Extend the model to support cities outside the US and cover a broader range of geographic locations.

Notes

• Generating the data took about 13 minutes (for 3269 US cities) on 8-cores (Intel 9700K), yielding 2,720,278 records (combinations of cities).
• Training on an Nvidia 3090 FE takes about an hour per epoch with an 80/20 test/train split. Batch size is 16, so there were 136,014 steps per epoch
• Evaluation on the above hardware took about 15 minutes for 20 epochs at 10k samples each.
• WARNING: It is unclear how the model performs on sentences, as it was trained and evaluated only on word-pairs. See improvement (5) above.