Add dataset card

README.md

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+---
+license: apache-2.0
+language: [en]
+task_categories: [text-generation]
+tags: [text-to-sql, oracle, accounts-payable, sql, chat]
+pretty_name: AP Text-to-SQL (Oracle) PEFT dataset
+size_categories: [1K<n<10K]
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: train.jsonl
+  - split: validation
+    path: val.jsonl
+  - split: test
+    path: test.jsonl
+---
+
+# ap-sql-peft
+
+A chat-format text-to-SQL dataset for Accounts Payable analytics on Oracle. The schema is modelled on Oracle Fusion
+AP, Payments and Supplier tables, and the data behind it is synthetic. The dataset was used to train the LoRA adapter
+[`samrat-kar/ap-sql-v1`](https://huggingface.co/samrat-kar/ap-sql-v1).
+
+Every gold SQL query was run against the demo database when the dataset was built; `meta.result_rows` records how many rows it returned.
+
+## Format
+
+One JSON object per line:
+
+```json
+{"messages": [{"role": "system", "content": "...rules + CREATE TABLE DDL + glossary..."},
+              {"role": "user", "content": "question (or follow-up / repair request)"},
+              {"role": "assistant", "content": "```sql\nSELECT ...\n```"}],
+ "meta": {"id": "...", "tier": "T1-T5", "family": "...", "kind": "ask|followup|repair-exec|repair-parse",
+          "source": "...", "group": "...", "tables": ["..."], "result_rows": 3, "approx_prompt_tokens": 3089, "split": "..."}}
+```
+
+The system prompt is the one the assistant builds at runtime: the rules, then the DDL of the retrieved tables
+(wide tables pruned to join keys plus the relevant columns), then the glossary entries. It is capped at about
+3,584 tokens so the whole prompt fits a 4,096-token window.
+
+## Splits
+
+| tier | train | validation | test |
+|---|---|---|---|
+| T1 (single table, simple) | 284 | 17 | 37 |
+| T2 | 463 | 28 | 56 |
+| T3 | 471 | 34 | 58 |
+| T4 | 143 | 10 | 19 |
+| T5 (hardest) | 80 | 6 | 9 |
+| **total** | **1,441** | **95** | **179** |
+
+- **Split by `meta.group`.** Paraphrases and variants of one question always land in the same split, so no test group
+  appears in train.
+- **Kinds.** `ask` is a plain question. `followup` carries conversation history. `repair-exec` and `repair-parse`
+  give the model a failed query and its Oracle or parse error, and the target is the corrected query.
+- **Sources.** `S2-templated` rows are generated from templates, `S6-repair` rows are repair cases, and
+  `S1-golden` rows are hand-written (test split only).
+
+## Usage
+
+```python
+from datasets import load_dataset
+ds = load_dataset("samrat-kar/ap-sql-peft")
+```
+
+## Limitations
+
+- Covers one synthetic schema.
+- Most rows are templated, so question phrasing is less varied than real user traffic.
+- Oracle SQL dialect only.