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@ -13,4 +13,8 @@ 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. - 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. - 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. # notes
- see `Makefile` for instructions.
- 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
- **TODO**`**: Need to add training / validation examples that involve city names in the context of sentences. _It is unclear how the model performs on sentences, as it was trained only on word-pairs.