Create README.md

README.md

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+---
+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](https://overturemaps.org/) and [Natural Earth](https://www.naturalearthdata.com/) 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:
+ 
+```json
+{
+  "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.
+Full generation pipeline: `github.com/developmentseed/gazet/dataset`
+
+## Trained model
+`developmentseed/gazet-model` - Qwen3.5-0.8B finetuned on this dataset