Spaces:
Running
Running
Commit History
-
Find cities Overture has only as a point, and rank the largest first
London, UK has no polygon in any Overture release: its locality is a point with no division_area, and there is no Greater London level between England and its boroughs. A search for "London" returned Canada, US towns, Finland and Kiribati. About 25 cities of 3M+ are in the same position (#14). GHSL's Urban Centre Database (GHS-UCDB R2024A) is now a fuzzy-only source, urban_centres: 11,422 named city polygons, each with alternate names and a population. It stays a source of its own, with ghsl_-prefixed ids, so every result traces back to one dataset. normalize_geodata reprojects it from Mollweide and strips the byte-order mark GHSL puts on every column and value. Equal scores now break by population (#12, item 2), in each source and when the API merges them, so "London" puts the UK's first. Overture's population is joined from its division records at normalize time; a place without one sorts last. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>b20c32c
-
Match every name a place has, without case or accents
Three of the misses in #12 were in the index all along, under another spelling: Chittagong is filed as Chattogram District, Da Nang as Đà Nẵng, Bogota as Bogotá, D.c. Search compared the query with the primary and English names only, and Jaro-Winkler rewards a shared prefix, so a near-homograph on another continent won. A record is now matched under every name Overture holds for it: its own, its translations (names.common) and its alternate, short and official names (names.rules), each compared after lowercasing and stripping accents. matched_name reports the spelling that hit. Natural Earth, which has no alternates, is matched on its own and English names as before, folded the same way. Building and folding those names on every query would make search about twice as slow as main, so normalize_geodata stores them in a search_names column, and search reads it where every file has one. A raw Overture download still works; it builds the names on the fly. On the 2026-08-19.0 release a fu…30114d9 -
Let fuzzy search find cities, and keep them from the model
A well-known city could be missing from every answer: normalisation kept only country, region and county, and whether a city survives that depends on how its country maps onto those levels. Brazil and Vietnam have no county rows, so none of their cities were searchable (#12). normalize_geodata now also writes Overture's locality and localadmin areas, to overture_normalized/localities/ rather than into divisions_area. Only mode=fuzzy searches that file. The natural-language pipeline, the SQL it generates and the training data all read divisions_area alone, since the model was never trained on these subtypes. /geometry/{id} resolves a locality, because its id only ever comes from a fuzzy result. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>41e01af
-
fix: filter only inland geometry for overture
f9e38fd
-
Randomize candidate dataset order
582d1ab -
fix: reduce templates to country, region & county for mvp
3ba9557
-
chore: run v3 dataset creation
2486fea -
Lowercase subtype names, fix question type hints
a5fd57d -
Fix: No pairs are created for mixed queries
- Normalize overture & natural earth to EPSG:4326 - Add better adjancency matrix to increase the overlap in query pairs - Fix titled & lower case subtype names in NE - Reduce number of threads in duckdb to prevent memory issuesdfb9466
-
enh: Add templates to handle queries like subregion, region
ca28f70 -
prompt: Add notes on how to pick places from query better
d237392 -
fix: mixed templates are not generated because of stale parquet files
8364f3c
-
chore: clean up the file references, remove unused scripts for gemma finetuning
45e223f
-
Add a couple of more sql templates for natural earth & chained queries
cb256f8 -
chore: clean sql generation, use conversation format, move prompts from user to system
c77ca5f
-
Add finetune instructions for gemma
1104031
-
ENH: Add dataset creation on scale using modal clusters
c88725f -
FEAT: Add to create SLM training data
d8d4856