Datasets:

developmentseed
/

gazet-dataset

license: mit
task_categories:
  - text-generation
language:
  - en
tags:
  - text-to-sql
  - geospatial
  - geocoding
  - duckdb
  - synthetic
size_categories:
  - 10K<n<100K

Gazet Dataset

Synthetic training data for finetuning small language models on geospatial tasks over Overture Maps and Natural Earth parquet datasets.

Tasks

SQL generation (`sql/`)

Input: user query + fuzzy-matched candidate entities (CSV)
Output: DuckDB spatial SQL query

Place extraction (`places/`)

Input: natural language query
Output: structured JSON with place names, country codes, and subtypes

Format

Each JSONL row is a conversation in chat-template format:

{
  "messages": [
    {"role": "system", "content": "..."},
    {"role": "user", "content": "..."},
    {"role": "assistant", "content": "..."}
  ]
}

Splits

Task	Train	Val	Test
SQL	`sql/train.jsonl`	`sql/val.jsonl`	`sql/test.jsonl`
Places	`places/train.jsonl`	`places/val.jsonl`	`places/test.jsonl`

See stats.json for per-family sample counts.

Generation

Data is generated from SQL templates applied to real Overture/Natural Earth spatial relations (adjacency, containment, intersection, etc.). Templates produce both the training SQL and the natural language question.

Code & Development

This model was trained and evaluated using code in the developmentseed/gazet GitHub repository.

Trained model

developmentseed/gazet-model - Qwen3.5-0.8B finetuned on this dataset