From c7b294c557432af14ef52b0b499b9ce903415f0a Mon Sep 17 00:00:00 2001 From: mm Date: Thu, 4 May 2023 10:13:12 +0000 Subject: [PATCH] readme --- README.md | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) create mode 100644 README.md diff --git a/README.md b/README.md new file mode 100644 index 0000000..5a4521a --- /dev/null +++ b/README.md @@ -0,0 +1,16 @@ +# city-transformers + +Generates dataset of cities (US only for now) and their geodesic distances. +Uses that dataset to fine-tune a neural-net to understand that cities closer to one another are more similar. +Distances become `labels` through the formula `1 - distance/MAX_DISTANCE`, where `MAX_DISTANCE=20_037.5 # km` represents half of the Earth's circumfrence. + +There are other factors that can make cities that are "close together" on the globe "far apart" in reality, due to political borders. +Factors like this are not considered in this model, it is only considering geography. + +However, for use-cases that involve different measures of distances (perhaps just time-zones, or something that considers the reality of travel), the general principals proven here should be applicable (pick a metric, generate data, train). + +A particularly useful addition to the dataset here: +- airports: they (more/less) have unique codes, and this semantic understanding would be helpful for search engines. +- aliases for cities: the dataset used for city data (lat/lon) contains a pretty exhaustive list of aliases for the cities. It would be good to generate examples of these with a distance of 0 and train the model on this knowledge. + +see `Makefile` for instructions.