updating readme

README.md

@@ -10,4 +10,69 @@ tags:
 pretty_name: AtlasSQL-BR
 size_categories:
 - 1K<n<10K
----
+---
+
+# AtlasSQL-BR
+
+AtlasSQL-BR is a novel geospatial Text-to-SQL dataset in Brazilian Portuguese, built upon real-world open-government data. Integrating school census data, public facility registries, and Brazil's official geographic boundary hierarchies, this dataset provides a robust benchmark for evaluating and training models on spatial queries.
+
+## 📊 Dataset Architecture and Structure
+
+The database schema underlying AtlasSQL-BR is organized around thematic public facility tables and their associated geographic boundary layers. This multi-level structure requires models to reason about spatial containment across different administrative granularities within a single SQL statement.
+
+* **Thematic Tables (Point Geometries):** The core table is `microdados_ed_basica`, derived from the Brazilian School Census, containing hundreds of infrastructural and pedagogical attributes per school. The schema also includes georeferenced records for other public facilities: libraries, CRAS, CREAS, and Centros POP.
+* **Boundary Tables (Polygon Geometries):** The dataset incorporates all seven official geographic boundary levels from IBGE: country, macro-region, state, municipality, district, neighborhood, and street.
+* **Spatial Operators:** Spatial relationships between facility points and boundary polygons are resolved using PostGIS extensions. The dataset covers a vocabulary of 22 PostGIS spatial functions (e.g., `ST_Intersects`, `ST_Within`, `ST_Contains`, `ST_Distance`).
+
+### Data Fields
+Based on the dataset structure, each record contains the following fields:
+* `question`: The natural language query in Brazilian Portuguese.
+* `sql_code`: The corresponding executable PostGIS/SQL query.
+* `territorial_division`: The administrative boundary level relevant to the query.
+* `level`: The structural complexity tier of the query.
+* `geospatial_functions`: The primary PostGIS functions required to solve the query.
+* `source`: Indicates the origin of the data. It can be `base_dataset` (the original 980 manually annotated questions) or partitions generated by data augmentation techniques (such as `delete`, `insert`, `swap`, `synonym`, `translate`).
+
+## 📈 Complexity Tiers
+
+The dataset comprises manually annotated question-SQL pairs classified into four complexity tiers based on the structural properties of the SQL query (all criteria are AND-combined within each tier):
+
+| Tier | Structural Criteria |
+| :--- | :--- |
+| **Easy** | <1 JOIN; no aggregations; no subqueries; direct spatial filter. |
+| **Medium** | 2-3 JOINs; no aggregations; multiple spatial conditions. |
+| **Hard** | 3-4 JOINs; >1 aggregation (COUNT, SUM, AVG); multiple spatial functions. |
+| **Very Hard** | 4+ JOINs; multiple aggregations; subqueries and/or CTEs; window functions (ROW_NUMBER, RANK, DENSE_RANK); CASE WHEN; possible UNION. |
+
+## 📁 Files and Reproducibility
+
+The dataset is divided into standard `train.parquet` and `validation.parquet` splits. 
+
+**⚠️ Important Note on Reproducibility:**
+The baseline experiments documented in the original paper were conducted **exclusively** on the original 980 question-SQL pairs. The dataset files available here also include additional data generated through data augmentation techniques. 
+
+To exactly replicate the experiments and results from the original paper, you **must**:
+1. Use the dataset version associated with the **`paper-released`** tag.
+2. Filter the data to include **only** the records where the `source` column is exactly `base_dataset`. The other partitions (`delete`, `insert`, `swap`, `synonym`, `translate`) contain synthetic augmented data and should be excluded when reproducing the paper's base results.
+
+### How to load the dataset for reproducibility (Python)
+
+You can easily download and filter the dataset using the Hugging Face `datasets` library to match the exact setup used in the paper:
+
+```python
+from datasets import load_dataset
+
+# 1. Load the dataset using the specific tag for the paper's release
+dataset = load_dataset("datafromlopes/atlas-sql-br", revision="paper-released")
+
+# 2. Filter both train and validation splits to include only the original 980 questions
+base_train = dataset["train"].filter(lambda x: x["source"] == "base_dataset")
+base_validation = dataset["validation"].filter(lambda x: x["source"] == "base_dataset")
+
+print(f"Original Train size: {len(base_train)}")
+print(f"Original Validation size: {len(base_validation)}")
+```
+
+## ✒️ Citation
+
+If you find this dataset useful for your research, please cite the original paper: