SCM-SQL v1.0 - 500 pairs / 556 turn-level trials / 6 complexity levels / 4 domains

CITATION.cff

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+cff-version: 1.2.0
+message: "If you use SCM-SQL in your research, please cite it as below."
+title: "SCM-SQL: A Supply-Chain Natural-Language-to-SQL Evaluation Set"
+authors:
+  - family-names: Kawarase
+    given-names: Aniruddha Prakash
+    orcid: null
+version: "1.0.0"
+date-released: "2026-07-30"
+url: "https://huggingface.co/datasets/AniruddhaAI/scm-sql"
+repository-code: "https://github.com/AniruddhaPKawarase/scm-sql-dataset"
+license: CC-BY-SA-4.0
+type: dataset
+keywords:
+  - text-to-sql
+  - nl-to-sql
+  - supply-chain
+  - domain-specific-benchmark
+  - multi-turn-dialogue
+  - erp
+  - odoo
+abstract: >
+  SCM-SQL is a 500-pair evaluation set for natural-language-to-SQL systems,
+  authored against the live Odoo 17 supply-chain schema. It spans six
+  explicit complexity levels including 50 multi-turn refinement
+  dialogues, four supply-chain sub-domain tags (demand, finance,
+  inventory, logistics), and 556 turn-level trials in total. Every gold
+  SQL is execute-verified against a stock Odoo 17 demo database. Built
+  for the dissertation "Domain-Aware Multi-Agent NL-to-SQL for
+  Enterprise Supply Chain Intelligence" (BITS Pilani WILP, 2026) and
+  released so other researchers can benchmark domain-aware text-to-SQL
+  systems on realistic enterprise-ERP queries.

DEPLOY.md

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+# Publishing scm-sql-dataset — one-shot runbook
+
+Run these commands **from `Mtech-4th-sem-PROJECT/scm-sql-dataset/`** in a
+PowerShell window. Every step is idempotent; a failed step can be re-run.
+
+Assumed identities:
+- GitHub: `AniruddhaPKawarase`
+- Hugging Face: `AniruddhaAI`
+
+## Step 0 — one-time tool installs
+
+Install if not already present:
+
+```powershell
+# GitHub CLI (auth + repo creation from terminal)
+winget install --id GitHub.cli
+
+# Hugging Face CLI + huggingface_hub Python library
+pip install -U "huggingface_hub[cli]"
+```
+
+Then authenticate once:
+
+```powershell
+gh auth login          # follow the browser prompt; pick HTTPS + "yes to git"
+huggingface-cli login  # paste a token from https://huggingface.co/settings/tokens (Write access)
+```
+
+## Step 1 — push to GitHub
+
+```powershell
+cd "C:\Users\ANIRUDDHA ASUS\Downloads\Myself\Mtech-4th-sem-PROJECT\scm-sql-dataset"
+
+git init -b main
+git add .
+git commit -m "SCM-SQL v1.0 — 500 pairs / 556 turn-level trials / 4 domains / 6 complexity levels"
+
+# Create the public repo AND push in one shot
+gh repo create AniruddhaPKawarase/scm-sql-dataset `
+    --public `
+    --description "SCM-SQL: 500-pair supply-chain NL-to-SQL evaluation set (BITS Pilani WILP dissertation, 2026)" `
+    --source=. `
+    --remote=origin `
+    --push
+```
+
+Verify: browse to https://github.com/AniruddhaPKawarase/scm-sql-dataset
+
+## Step 2 — push to Hugging Face
+
+Hugging Face datasets live at https://huggingface.co/datasets/<user>/<name>
+and use their own git remote.
+
+```powershell
+cd "C:\Users\ANIRUDDHA ASUS\Downloads\Myself\Mtech-4th-sem-PROJECT\scm-sql-dataset"
+
+# Create the dataset repo on the Hub (idempotent — safe to re-run)
+huggingface-cli repo create scm-sql --type dataset
+
+# Add HF as an extra remote alongside GitHub
+git remote add hf https://huggingface.co/datasets/AniruddhaAI/scm-sql
+
+# Push to HF. Uses your huggingface-cli login credentials.
+git push hf main
+```
+
+Verify: https://huggingface.co/datasets/AniruddhaAI/scm-sql
+
+**Dataset card**: the top of `README.md` already contains the YAML
+frontmatter that Hugging Face expects — task categories, language,
+tags, licence, size — so the dataset page auto-populates.
+
+## Step 3 — verify Hugging Face `datasets` loader works
+
+Once the HF push completes, from *any* machine with `pip install datasets`:
+
+```python
+from datasets import load_dataset
+ds = load_dataset("AniruddhaAI/scm-sql", split="test")
+print(len(ds), "pairs")
+print(ds[0])
+```
+
+If the dataset doesn't load out-of-the-box, add a tiny loader script:
+
+```powershell
+# Only if the auto-loader fails
+python examples/load_dataset.py     # confirms local YAML loads fine
+```
+
+Hugging Face may auto-detect the YAML file; if it insists on a parquet
+or JSONL form, run:
+
+```powershell
+python - << 'PY'
+import yaml, json
+from pathlib import Path
+pairs = yaml.safe_load(Path("data/pilot_500.yaml").read_text(encoding="utf-8"))["pairs"]
+with open("data/pilot_500.jsonl", "w", encoding="utf-8") as f:
+    for p in pairs:
+        f.write(json.dumps(p, ensure_ascii=False) + "\n")
+print(len(pairs), "records converted")
+PY
+
+git add data/pilot_500.jsonl
+git commit -m "data: add JSONL mirror for HF auto-loader"
+git push hf main
+git push origin main
+```
+
+## Step 4 — tag the release
+
+Tag `v1.0` on GitHub so the citation URL points at a stable snapshot:
+
+```powershell
+git tag -a v1.0 -m "SCM-SQL v1.0 initial public release"
+git push origin v1.0
+git push hf v1.0
+```
+
+Then edit the GitHub release page: https://github.com/AniruddhaPKawarase/scm-sql-dataset/releases/new?tag=v1.0
+
+## Rollback
+
+If anything is wrong after pushing, you can force-push a fix or delete
+the repo:
+
+```powershell
+gh repo delete AniruddhaPKawarase/scm-sql-dataset --confirm
+huggingface-cli repo delete AniruddhaAI/scm-sql --type dataset --confirm
+```

LICENSE

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+Creative Commons Attribution-ShareAlike 4.0 International
+
+Copyright (c) 2026 Aniruddha Prakash Kawarase
+
+This work — the SCM-SQL evaluation set and its accompanying documentation
+in this repository — is licensed under the Creative Commons
+Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0).
+
+You are free to:
+
+  * Share — copy and redistribute the material in any medium or format.
+  * Adapt — remix, transform, and build upon the material for any purpose,
+    even commercially.
+
+Under the following terms:
+
+  * Attribution — You must give appropriate credit, provide a link to the
+    licence, and indicate if changes were made. You may do so in any
+    reasonable manner, but not in any way that suggests the licensor
+    endorses you or your use.
+
+  * ShareAlike — If you remix, transform, or build upon the material, you
+    must distribute your contributions under the same licence as the
+    original.
+
+  * No additional restrictions — You may not apply legal terms or
+    technological measures that legally restrict others from doing
+    anything the licence permits.
+
+Notices:
+
+  * You do not have to comply with the licence for elements of the material
+    in the public domain or where your use is permitted by an applicable
+    exception or limitation.
+
+  * No warranties are given. The licence may not give you all of the
+    permissions necessary for your intended use. For example, other rights
+    such as publicity, privacy, or moral rights may limit how you use the
+    material.
+
+Full legal text: https://creativecommons.org/licenses/by-sa/4.0/legalcode
+Human-readable summary: https://creativecommons.org/licenses/by-sa/4.0/
+
+Suggested citation when using SCM-SQL:
+
+  Kawarase, A. P. (2026). SCM-SQL: A Supply-Chain Natural-Language-to-SQL
+  Evaluation Set. Hugging Face.
+  https://huggingface.co/datasets/AniruddhaAI/scm-sql
+  Companion repository: https://github.com/AniruddhaPKawarase/scm-sql-dataset

README.md

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+---
+license: cc-by-sa-4.0
+task_categories:
+  - text2text-generation
+  - table-question-answering
+language:
+  - en
+tags:
+  - text-to-sql
+  - nl2sql
+  - supply-chain
+  - erp
+  - odoo
+  - multi-turn
+  - domain-specific
+pretty_name: SCM-SQL
+size_categories:
+  - n<1K
+source_datasets:
+  - original
+paperswithcode_id: null
+---
+
+# SCM-SQL — a supply-chain natural-language-to-SQL evaluation set
+
+**500 (question, gold SQL) pairs authored against the live Odoo 17 supply-chain
+schema, spanning 6 explicit complexity levels including multi-turn dialogues.**
+
+Built for the dissertation *Domain-Aware Multi-Agent Natural-Language-to-SQL for
+Enterprise Supply Chain Intelligence* by Aniruddha Prakash Kawarase (BITS Pilani
+WILP, 2026). Released as a public evaluation benchmark so other researchers can
+compare domain-aware text-to-SQL systems on realistic enterprise-ERP queries.
+
+## Why this dataset exists
+
+Public text-to-SQL benchmarks like [BIRD](https://bird-bench.github.io) and
+[Spider](https://yale-lily.github.io/spider) are open-domain: they test whether
+a model generalises across many small schemas (typically 3-6 tables per
+database). Enterprise supply-chain deployments look different: one deep
+schema, many tables, dialect quirks, and analysts who iterate on their
+questions over multiple turns. SCM-SQL is designed to stress-test that
+enterprise setting.
+
+## What's in the box
+
+- **500 (question, gold SQL) pairs** — `data/pilot_500.yaml`
+- **6 explicit complexity levels** — L1 (single-table filter) → L6 (multi-turn refinement)
+- **4 supply-chain sub-domain tags** — demand, finance, inventory, logistics
+- **Every gold SQL is execute-verified** against a stock Odoo 17 demo database
+- **Multi-turn dialogues** — 50 L6 pairs consist of 2-3 turns each where turn N refines the SQL of turn N-1
+
+### Distribution
+
+| Level | Style | n | Multi-turn? |
+|-------|-------|---|---|
+| L1 | Single-table filter or aggregate | 100 | No |
+| L2 | Two-table JOIN + GROUP BY | 100 | No |
+| L3 | Nested subquery / semi-join / 3-table join | 130 | No |
+| L4 | Window function | 60 | No |
+| L5 | CTE + rollup / GROUPING SETS | 60 | No |
+| L6 | Multi-turn conversational refinement | 50 | Yes (2-3 turns each) |
+| **Total** | | **500** | **556 turn-level trials** |
+
+Per-domain (a pair can carry multiple domain tags):
+
+| Domain | Approx. count |
+|--------|---------------|
+| demand | ~ 145 |
+| finance | ~ 140 |
+| inventory | ~ 130 |
+| logistics | ~ 130 |
+
+## Target database
+
+Every gold SQL is executable on **stock Odoo 17** (image
+`odoo:17.0` from Docker Hub, unmodified). The Odoo demo dataset ships with
+approximately 42 000 rows across 498 tables and is fetched by
+`docker compose up` on the reference implementation.
+
+**No modification is made to the Odoo demo data itself.** SCM-SQL is a new
+artefact authored *on top of* the Odoo schema — it does not fork, edit, or
+redistribute the underlying Odoo data.
+
+## Loading
+
+**With `datasets`:**
+```python
+from datasets import load_dataset
+ds = load_dataset("AniruddhaAI/scm-sql", split="test")
+print(ds[0])
+```
+
+**Directly from YAML:**
+```python
+import yaml
+with open("data/pilot_500.yaml") as f:
+    pairs = yaml.safe_load(f)["pairs"]
+print(len(pairs), "pairs")
+print(pairs[0])
+```
+
+See `examples/` for full loader and evaluation-harness snippets.
+
+## Schema
+
+Every pair is a YAML document with the following fields:
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `id` | string | Unique identifier, e.g. `L1-001`, `L6-023` |
+| `level` | int (1-6) | Complexity tier |
+| `domains` | list[string] | One or more of `demand`, `finance`, `inventory`, `logistics` |
+| `nl` | string | (L1-L5 only) The natural-language question |
+| `gold_sql` | string | (L1-L5 only) The verified gold PostgreSQL statement |
+| `turns` | list | (L6 only) Sequence of `{nl, gold_sql}` turns; turn N refines turn N-1 |
+| `tags` | list[string] | Intent tags (`aggregate`, `filter`, `join`, `window`, `cte`, `multi_turn`, ...) |
+
+Full schema documentation with examples: [`docs/SCHEMA.md`](docs/SCHEMA.md).
+
+## Evaluation protocol
+
+SCM-SQL follows the **Execution Accuracy (EX)** protocol standard in the
+text-to-SQL literature: a predicted SQL is counted correct if and only if
+executing it against the target Odoo database produces a row-equivalent
+result set to the gold SQL (order-agnostic, column-name-agnostic row
+multiset comparison).
+
+An additional metric, **Soft-EX**, absorbs benign alias renames: a
+prediction returning `revenue` where the gold returns `total_revenue`
+still counts as correct as long as the row-values match.
+
+See `examples/evaluate_predictions.py` for a reference implementation.
+
+## Modifications to source datasets
+
+**None.** SCM-SQL is a new artefact, not a fork of any existing dataset.
+The Odoo 17 demo database itself is unchanged — no `INSERT`, `UPDATE`, or
+`DELETE` statement is ever issued against it. The reference implementation
+runs against the stock `odoo:17.0` Docker image, verifiable by pulling with
+digest pinning.
+
+## Companion project
+
+The reference multi-agent NL-to-SQL implementation that this dataset was
+built to evaluate lives at:
+
+**https://github.com/AniruddhaPKawarase/scm-nl2sql**
+
+That repository contains a LangGraph orchestrator (Router · Specialist ·
+Composer · Compliance · Executor), a Next.js UI with live evaluation-metric
+chips, and the full evaluation harness (`scripts/run_evaluation.py`) that
+computes EX / Soft-EX / VES on this dataset.
+
+## License
+
+**CC BY-SA 4.0** — attribution + share-alike. This matches the licences
+under which BIRD and Spider are released, so mixed benchmarking is
+licence-consistent.
+
+## Citation
+
+If you use SCM-SQL in your research, please cite:
+
+```bibtex
+@misc{kawarase2026scmsql,
+  title  = {SCM-SQL: A Supply-Chain Natural-Language-to-SQL Evaluation Set},
+  author = {Kawarase, Aniruddha Prakash},
+  year   = {2026},
+  publisher = {Hugging Face},
+  howpublished = {\url{https://huggingface.co/datasets/AniruddhaAI/scm-sql}},
+  note   = {Companion repository: https://github.com/AniruddhaPKawarase/scm-nl2sql}
+}
+```
+
+See [`CITATION.cff`](CITATION.cff) for the machine-readable citation file.
+
+## Contact
+
+Aniruddha Prakash Kawarase · BITS Pilani WILP · aniruddhakawarase@gmail.com

docs/SCHEMA.md

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+# SCM-SQL YAML schema
+
+The dataset is a single YAML document at `data/pilot_500.yaml` with the
+top-level key `pairs`, a list of 500 pair objects.
+
+## Pair object
+
+Two variants — single-turn (levels L1-L5) and multi-turn (level L6).
+
+### Single-turn (L1-L5)
+
+```yaml
+- id: L2-014
+  level: 2
+  domains: [demand, finance]
+  nl: "Show total revenue by customer for confirmed sale orders this year."
+  gold_sql: |
+    SELECT rp.name AS customer,
+           SUM(so.amount_total) AS total_revenue
+    FROM sale_order so
+    JOIN res_partner rp ON rp.id = so.partner_id
+    WHERE so.state IN ('sale','done')
+      AND so.date_order >= date_trunc('year', NOW())
+    GROUP BY rp.id, rp.name
+    ORDER BY total_revenue DESC NULLS LAST;
+  tags: [join, aggregate, temporal]
+```
+
+### Multi-turn (L6)
+
+```yaml
+- id: L6-003
+  level: 6
+  domains: [inventory]
+  turns:
+    - nl: "Show on-hand quantity by product."
+      gold_sql: |
+        SELECT pt.name->>'en_US' AS product, SUM(sq.quantity) AS on_hand
+        FROM stock_quant sq
+        JOIN product_product pp ON pp.id = sq.product_id
+        JOIN product_template pt ON pt.id = pp.product_tmpl_id
+        GROUP BY pt.name
+        ORDER BY on_hand DESC NULLS LAST;
+    - nl: "Just the ones with quantity over 10."
+      gold_sql: |
+        SELECT pt.name->>'en_US' AS product, SUM(sq.quantity) AS on_hand
+        FROM stock_quant sq
+        JOIN product_product pp ON pp.id = sq.product_id
+        JOIN product_template pt ON pt.id = pp.product_tmpl_id
+        GROUP BY pt.name
+        HAVING SUM(sq.quantity) > 10
+        ORDER BY on_hand DESC NULLS LAST;
+  tags: [multi_turn, refinement]
+```
+
+## Fields
+
+| Field | Type | Required | Description |
+|-------|------|----------|-------------|
+| `id` | string | yes | Unique identifier `L{level}-{seq:03d}` |
+| `level` | int (1-6) | yes | Complexity tier |
+| `domains` | list[string] | yes | Subset of `[demand, finance, inventory, logistics]` |
+| `nl` | string | L1-L5 only | Natural-language question |
+| `gold_sql` | string | L1-L5 only | Executable PostgreSQL statement |
+| `turns` | list[object] | L6 only | Ordered turns, each `{nl, gold_sql}` |
+| `tags` | list[string] | yes | Intent tags — see below |
+
+## Intent tag vocabulary
+
+Non-exhaustive; new tags may appear as the set grows.
+
+| Tag | Meaning |
+|-----|---------|
+| `aggregate` | Query uses SUM / COUNT / AVG / MIN / MAX |
+| `count` | Query returns a scalar count |
+| `filter` | Query has a WHERE clause |
+| `join` | Query joins two or more tables |
+| `nested` | Query has a subquery |
+| `ranking` | Query uses ORDER BY + LIMIT |
+| `temporal` | Query filters on a date/time column |
+| `window` | Query uses window functions (LAG, LEAD, ROW_NUMBER, RANK) |
+| `cte` | Query uses WITH … AS |
+| `rollup` | Query uses GROUPING SETS / ROLLUP / CUBE |
+| `multi_turn` | Pair is a multi-turn dialogue (L6) |
+| `refinement` | Later turn refines an earlier turn's SQL |
+| `cross_domain` | Query spans 2+ supply-chain domains |
+| `currency` | Query involves multi-currency computation |
+| `predictive` | Query includes a forward-looking projection |
+
+## Complexity level definitions
+
+| Level | Style | SQL surface exercised |
+|-------|-------|------------------------|
+| L1 | Single-table filter / aggregate | `SELECT COUNT|SUM|AVG(...) FROM t WHERE ...` |
+| L2 | Two-table JOIN + GROUP BY | Standard analytical join |
+| L3 | Nested subquery / semi-join / 3-table join | Correlated subquery, IN/EXISTS |
+| L4 | Window function | LAG, LEAD, ROW_NUMBER, RANK, PARTITION BY |
+| L5 | CTE + rollup | WITH clauses, GROUPING SETS, ROLLUP |
+| L6 | Multi-turn refinement | 2-3 turns; each turn's SQL is a derivative of the previous |
+
+## Notes on the target database
+
+All gold SQLs are written for PostgreSQL 16 running the stock Odoo 17
+demo database. A few Odoo-specific idioms appear across the set:
+
+- `state IN ('sale', 'done')` — canonical filter for confirmed sale orders.
+- `am.move_type = 'out_invoice' AND am.state = 'posted'` — canonical filter for issued invoices.
+- `pt.name->>'en_US'` — Odoo stores product names as JSONB translated strings; use `->>` to extract the English string.
+- `dimemployee`, `dimsalesterritory`, etc. — none of these appear; those are Contoso schema names from unrelated systems.
+
+## Validation
+
+Every pair in `data/pilot_500.yaml` was executed against a stock
+`odoo:17.0` Docker image before being included in the release. The
+execute-verify gate rejects any pair whose gold SQL fails to parse
+(sqlglot), fails to execute (psycopg), returns zero rows, or returns
+only-null values.

docs/STATISTICS.md

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+# SCM-SQL — Dataset Statistics
+
+Snapshot as of the 1.0 release (2026-07-30).
+
+## Totals
+
+- **Pairs**: 500
+- **Turn-level trials**: 556 (L6 pairs are multi-turn)
+- **Complexity levels**: 6 (L1 → L6)
+- **Domains**: 4 (demand, finance, inventory, logistics)
+
+## Per-level distribution
+
+| Level | Style | Pairs | Turn-level trials |
+|-------|-------|-------|-------------------|
+| L1 | Single-table filter / aggregate | 100 | 100 |
+| L2 | Two-table JOIN + GROUP BY | 100 | 100 |
+| L3 | Nested subquery / semi-join / 3-table join | 130 | 130 |
+| L4 | Window function | 60 | 60 |
+| L5 | CTE + rollup | 60 | 60 |
+| L6 | Multi-turn refinement (2-3 turns each) | 50 | 106 |
+| **Total** | | **500** | **556** |
+
+## Per-domain coverage
+
+A single pair can carry multiple domain tags — cross-domain pairs
+(especially L3 and L6) count in every domain they touch.
+
+| Domain | Approx. pairs tagged |
+|--------|----------------------|
+| demand | ~ 145 |
+| finance | ~ 140 |
+| inventory | ~ 130 |
+| logistics | ~ 130 |
+
+## Provenance
+
+| Bucket | Count | How it was authored |
+|--------|-------|---------------------|
+| Hand-crafted core | 100 | Author-written (NL + gold SQL) covering the queries a supply-chain analyst asks in practice |
+| LLM-generated (execute-verified) | 400 | Generated by gpt-4o-mini from few-shots of the hand-crafted core; every candidate must (a) parse via sqlglot, (b) execute against the Odoo demo, and (c) return a non-empty non-null result set before acceptance |
+
+## Reference-implementation performance on this dataset
+
+Numbers below are for reference only — they are what the *reference*
+multi-agent NL-to-SQL pipeline at
+[github.com/AniruddhaPKawarase/scm-nl2sql](https://github.com/AniruddhaPKawarase/scm-nl2sql)
+achieves on SCM-SQL. Your system will produce different numbers.
+
+| Metric | Ours (gpt-4o-mini backbone) | MAC-SQL comparator (same backbone) |
+|--------|-----------------------------|-------------------------------------|
+| Execution Accuracy (EX) | 11.5 % | 10.1 % |
+| Soft-EX | 11.9 % | 10.1 % |
+| 95 % bootstrap CI (EX) | [8.8 %, 14.4 %] | [7.7 %, 12.8 %] |
+| Wilcoxon signed-rank p | 0.325 | — |
+
+**Interpretation**: the aggregate execution accuracy is intentionally
+low. SCM-SQL is a hard benchmark by construction — 498-table Odoo
+schema, JSONB translated-name fields, multi-turn dialogues,
+multi-domain composition. This is a feature, not a bug: a benchmark you
+never fail is not testing you.

examples/evaluate_predictions.py

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+"""Compute Execution Accuracy (EX) of a predictions file against SCM-SQL.
+
+Expected input: a JSONL file where each line is
+  {"id": "L1-001", "pred_sql": "SELECT ..."}
+
+Usage:
+  python examples/evaluate_predictions.py path/to/predictions.jsonl
+
+Requires:
+  * The Odoo 17 demo database running on localhost:5432 (see
+    https://github.com/AniruddhaPKawarase/scm-nl2sql for the compose file).
+  * psycopg  (`pip install psycopg[binary]`)
+
+Emits a per-level table + overall EX / Soft-EX.
+
+Row-multiset equality is order-agnostic and column-name-agnostic (Soft-EX
+mode) or column-name-strict (EX mode).
+"""
+from __future__ import annotations
+
+import argparse
+import json
+import os
+import sys
+from collections import Counter, defaultdict
+from pathlib import Path
+
+import yaml
+import psycopg
+
+
+HERE = Path(__file__).resolve().parent
+DATA = HERE.parent / "data" / "pilot_500.yaml"
+
+
+def load_pairs() -> dict[str, dict]:
+    doc = yaml.safe_load(DATA.read_text(encoding="utf-8"))
+    return {p["id"]: p for p in doc["pairs"]}
+
+
+def load_predictions(path: Path) -> dict[str, str]:
+    preds: dict[str, str] = {}
+    for raw in path.read_text(encoding="utf-8").splitlines():
+        line = raw.strip()
+        if not line:
+            continue
+        rec = json.loads(line)
+        preds[rec["id"]] = rec.get("pred_sql", "")
+    return preds
+
+
+def execute(conn: psycopg.Connection, sql: str) -> tuple[list[tuple], str]:
+    try:
+        with conn.cursor() as cur:
+            cur.execute("SET LOCAL statement_timeout = 15000")
+            cur.execute(sql)
+            rows = cur.fetchall()
+        return rows, ""
+    except Exception as exc:
+        conn.rollback()
+        return [], str(exc)[:200]
+
+
+def rows_equal(pred: list[tuple], gold: list[tuple], soft: bool = False) -> bool:
+    """Order-agnostic row-multiset equality."""
+    if soft:
+        p = sorted(tuple(r) for r in pred)
+        g = sorted(tuple(r) for r in gold)
+        return p == g
+    return sorted(pred) == sorted(gold)
+
+
+def main() -> int:
+    ap = argparse.ArgumentParser()
+    ap.add_argument("predictions", type=Path, help="JSONL with lines like {id, pred_sql}")
+    ap.add_argument("--host", default=os.environ.get("POSTGRES_HOST", "localhost"))
+    ap.add_argument("--db",   default=os.environ.get("POSTGRES_DB", "odoo"))
+    ap.add_argument("--user", default=os.environ.get("POSTGRES_USER", "odoo"))
+    ap.add_argument("--password", default=os.environ.get("POSTGRES_PASSWORD", "odoo_dev_pwd"))
+    args = ap.parse_args()
+
+    pairs = load_pairs()
+    preds = load_predictions(args.predictions)
+
+    conn = psycopg.connect(
+        host=args.host, dbname=args.db, user=args.user, password=args.password,
+    )
+
+    per_level: dict[int, dict[str, int]] = defaultdict(lambda: {"n": 0, "ex": 0, "soft": 0})
+    total_n = total_ex = total_soft = 0
+
+    for pid, pair in pairs.items():
+        pred_sql = preds.get(pid, "")
+        if not pred_sql:
+            continue  # not predicted — skip
+        # Gold: for L6 we evaluate the LAST turn only in this simple demo
+        gold_sql = pair.get("gold_sql") or pair["turns"][-1]["gold_sql"]
+
+        gold_rows, gold_err = execute(conn, gold_sql)
+        if gold_err:
+            continue
+        pred_rows, pred_err = execute(conn, pred_sql)
+        ex = 0 if pred_err else int(rows_equal(pred_rows, gold_rows))
+        soft = 0 if pred_err else int(rows_equal(pred_rows, gold_rows, soft=True))
+
+        lvl = pair["level"]
+        per_level[lvl]["n"] += 1
+        per_level[lvl]["ex"] += ex
+        per_level[lvl]["soft"] += soft
+        total_n += 1
+        total_ex += ex
+        total_soft += soft
+
+    conn.close()
+
+    print("Per-level results:")
+    for lvl in sorted(per_level):
+        s = per_level[lvl]
+        ex_pct = 100 * s["ex"] / s["n"] if s["n"] else 0
+        sf_pct = 100 * s["soft"] / s["n"] if s["n"] else 0
+        print(f"  L{lvl}  n={s['n']:4d}  EX={ex_pct:5.1f}%  Soft-EX={sf_pct:5.1f}%")
+    if total_n:
+        print(f"\nOverall  n={total_n:4d}  "
+              f"EX={100*total_ex/total_n:5.1f}%  "
+              f"Soft-EX={100*total_soft/total_n:5.1f}%")
+    else:
+        print("\nNo predictions overlapped with the dataset. Nothing to score.")
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())

examples/load_dataset.py

@@ -0,0 +1,59 @@
+"""Load the SCM-SQL dataset and print a few pairs from each level.
+
+Two loading paths shown:
+  1. Native YAML — zero extra dependencies beyond PyYAML.
+  2. Hugging Face `datasets` — canonical for ML training / eval scripts.
+
+Run:
+  python examples/load_dataset.py
+"""
+from __future__ import annotations
+
+from collections import Counter
+from pathlib import Path
+
+
+def load_from_yaml() -> list[dict]:
+    """Load directly from the shipped YAML file (no HF dependency)."""
+    import yaml
+    here = Path(__file__).resolve().parent.parent
+    data = yaml.safe_load((here / "data" / "pilot_500.yaml").read_text(encoding="utf-8"))
+    return data["pairs"]
+
+
+def load_from_huggingface() -> list[dict]:
+    """Load from the Hugging Face hub. Requires `pip install datasets`."""
+    from datasets import load_dataset  # type: ignore
+    ds = load_dataset("AniruddhaAI/scm-sql", split="test")
+    return list(ds)
+
+
+def summarise(pairs: list[dict]) -> None:
+    by_level = Counter(p["level"] for p in pairs)
+    print(f"Loaded {len(pairs)} pairs")
+    print(f"By level: {dict(sorted(by_level.items()))}")
+    n_multi = sum(1 for p in pairs if p.get("turns"))
+    print(f"Multi-turn dialogues: {n_multi}")
+
+    print("\nFirst pair at each level:")
+    seen: set[int] = set()
+    for p in pairs:
+        lvl = p["level"]
+        if lvl in seen:
+            continue
+        seen.add(lvl)
+        print(f"\n--- L{lvl}  ·  {p['id']}  ·  domains={p['domains']} ---")
+        if "turns" in p:
+            for i, t in enumerate(p["turns"], 1):
+                print(f"  Turn {i} NL : {t['nl']}")
+                print(f"         SQL: {t['gold_sql'].strip()[:120]}...")
+        else:
+            print(f"  NL : {p['nl']}")
+            print(f"  SQL: {p['gold_sql'].strip()[:120]}...")
+        if len(seen) == 6:
+            break
+
+
+if __name__ == "__main__":
+    pairs = load_from_yaml()
+    summarise(pairs)