Explain training pairs and generation rules

README.md

@@ -35,3 +35,18 @@ configs:
   - split: train
     path: data/train-*
 ---
+
+# Table retrieval training data
+
+2,000 synthetic hard table retrieval pairs generated with GPT-6 Luna: 500 each of Vertical, Matrix, Hierarchy, and Nested.
+
+Each train row contains a query, its answer, a positive image, a style-matched hard negative image, the table type and domain, target cell metadata (JSON string), and negative transformation type. Both image columns embed PNG bytes in Parquet shards.
+
+Negative rules: Vertical swaps distinct values in one column; Matrix moves the queried entities onto the same row or column axis; Hierarchy swaps parent groups; Nested swaps outer groups. Rendering uses randomized Matplotlib, HTML/CSS, and LaTeX styles, preserved between each positive and negative.
+
+`metadata/` preserves source table specifications, query templates and answers, original render styles, and hard-negative records. Paths inside these original records describe the source layout; use the embedded image columns when loading the dataset.
+
+```python
+from datasets import load_dataset
+ds = load_dataset("NLPRETGOOD/table_train", split="train")
+```