Show HN: Open-source text-to-geolocation models(github.com) Yachay is an open-source community that works with the most accurate text-to-geolocation models on the market right now |
Show HN: Open-source text-to-geolocation models(github.com) Yachay is an open-source community that works with the most accurate text-to-geolocation models on the market right now |
We are working on adding more data as well - feel free to create a GitHub issue if there's more you need - we're going to be working on everything there is to do to help the developers here:)
I think something like this (but with more substance) could be helpful for some people, especially in the social sciences.
It's really a difficult task to parse text at large scale with accurate geographical tagging.
When processing text at large scale, the usefulness of heuristic approaches like the one we're discussing diminishes rapidly.
No, in many situations, something doesn’t have to be perfect to be useful.
Again, I think you are missing the original point being made:
> Depending upon your use-case, you can get pretty good results by…
You seem to be responding as if I said:
> For all use-cases, you can get flawless results by…
Pointing out that this is not perfect is irrelevant to the point I was making. “Good enough” is usually good enough.