README.md

---
license: cc-by-4.0
tags:
  - text-to-sql
  - nlp
  - synthetic-data
  - dataset-analysis
  - quality-metrics
  - sql
  - benchmarking
language:
  - en
pretty_name: "SQL NL Pairs — Coverage & Quality Analysis"
---

# Coverage Analysis: SQL Natural Language Pairs Synthetic Benchmark

> **Technical Report** · October 2026 · SaluteFace AAC  
> _Not peer reviewed. Computed on [SalutefaceAAC/sql-natural-language-pairs](https://huggingface.co/datasets/SalutefaceAAC/sql-natural-language-pairs) v1.0._

---

## Abstract

Synthetic text-to-SQL datasets are useful for fine-tuning and evaluation, but require principled quality assessment beyond simple scale metrics. We present a distributional coverage analysis of the *sql-natural-language-pairs* dataset using four complementary dimensions: **lexical diversity**, **SQL feature coverage**, **domain balance**, and **difficulty calibration**. We find the dataset achieves near-perfect domain balance (Shannon balance 0.96), zero exact duplicates, and well-calibrated difficulty (SQL token length scales monotonically: easy 10.0 → medium 25.5 → hard 35.8 tokens). Key gaps include question-starter concentration (four starters cover 80% of questions), underrepresentation of `MAX`/`MIN`/`COALESCE` aggregate functions, and a domain skew toward ecommerce (34.3%). We compute a **Composite Coverage Score of 0.78 / 1.0** and provide targeted gap-filling recommendations.

---

## 1. Dataset Under Analysis

| Property | Value |
|----------|-------|
| Dataset | [SalutefaceAAC/sql-natural-language-pairs](https://huggingface.co/datasets/SalutefaceAAC/sql-natural-language-pairs) |
| Total examples | 286 |
| Splits | 230 train / 28 val / 28 test |
| Domains | ecommerce, healthcare, analytics, hr, inventory |
| Query types | aggregate, join, select, subquery, window, cte |
| Difficulty tiers | easy, medium, hard |
| License | CC-BY-4.0 |

---

## 2. Methodology

All metrics are computed from the raw dataset using the HuggingFace `datasets` library. The analysis covers four dimensions:

1. **Lexical Diversity** — Distinct-N metrics, type-token ratio, near-duplicate detection via Jaccard similarity
2. **SQL Feature Coverage** — Keyword frequency for 12 SQL clauses and 7 aggregate functions
3. **Domain Balance** — Shannon entropy normalized to max entropy for 5 domains
4. **Difficulty Calibration** — SQL token count distribution per difficulty tier, query-type × difficulty cross-tabulation

Composite Coverage Score = arithmetic mean of four sub-scores (Deduplication, Domain Balance, Feature Coverage, Phrasing Diversity).

**Analysis code:**
```python
from datasets import load_dataset
import pandas as pd, re, math
from collections import Counter

ds = load_dataset('SalutefaceAAC/sql-natural-language-pairs')
dfs = [ds[s].to_pandas().assign(split=s) for s in ds]
df = pd.concat(dfs, ignore_index=True)

def tokenize(text):
    return re.findall(r'\b\w+\b', text.lower())

# Distinct-N
all_toks = [t for q in df['question'] for t in tokenize(q)]
distinct1 = len(set(all_toks)) / len(all_toks)

bigrams = [(a,b) for q in df['question'] for a,b in zip(tokenize(q), tokenize(q)[1:])]
distinct2 = len(set(bigrams)) / len(bigrams)

# Domain balance (Shannon entropy)
p = df['domain'].value_counts() / len(df)
H = -sum(x * math.log(x) for x in p)
balance = H / math.log(len(p))

print(f"Distinct-1: {distinct1:.3f}, Distinct-2: {distinct2:.3f}, Balance: {balance:.3f}")
```

---

## 3. Results

### 3.1 Lexical Diversity

| Metric | Value | Assessment |
|--------|-------|------------|
| Distinct-1 (unigrams) | 0.157 | Expected low for short domain-specific questions |
| Distinct-2 (bigrams) | 0.498 | Moderate — half of bigrams are unique |
| Type-token ratio | 0.157 | Narrow domain vocabulary |
| Avg question length | 8.8 tokens | Concise and focused |
| Question length range | 4–16 tokens | — |
| Exact duplicates | 0 | ✅ Clean |
| Near-duplicate rate (Jaccard > 0.7) | 0.5% | ✅ Excellent |

**Question starter distribution** (concentration risk: four starters = 80.3% of questions):

| Starter | Count | % |
|---------|-------|---|
| what | 67 | 23.4% |
| find | 59 | 20.6% |
| how | 57 | 19.9% |
| list | 47 | 16.4% |
| show | 17 | 5.9% |
| which | 14 | 4.9% |
| other (6 types) | 25 | 8.7% |

### 3.2 SQL Feature Coverage

**Clause frequency:**

| SQL Clause | Count | % | Status |
|------------|-------|---|--------|
| WHERE | 199 | 69.6% | ✅ Strong |
| JOIN | 109 | 38.1% | ✅ Good |
| ORDER BY | 86 | 30.1% | ✅ Good |
| GROUP BY | 72 | 25.2% | ✅ Good |
| LIMIT | 30 | 10.5% | ✅ OK |
| Subquery | 27 | 9.4% | ✅ OK |
| DISTINCT | 17 | 5.9% | ⚠️ Low |
| OVER (window) | 15 | 5.2% | ⚠️ Low |
| HAVING | 11 | 3.8% | ⚠️ Low |
| WITH (CTE) | 9 | 3.1% | 🔴 Gap |
| LEFT JOIN | 8 | 2.8% | 🔴 Gap |
| CASE | 4 | 1.4% | 🔴 Gap |

**Aggregate function coverage:**

| Function | Count | % | Status |
|----------|-------|---|--------|
| COUNT | 87 | 30.4% | ✅ Good |
| SUM | 52 | 18.2% | ✅ Good |
| AVG | 43 | 15.0% | ✅ Good |
| MIN | 5 | 1.7% | ⚠️ Low |
| MAX | 3 | 1.0% | 🔴 Gap |
| NULLIF | 2 | 0.7% | 🔴 Gap |
| COALESCE | 0 | 0.0% | 🔴 Missing |

### 3.3 Domain Balance

Shannon balance score: **0.9603** (1.0 = perfectly balanced across 5 domains)

| Domain | Count | % | Distance from ideal 20% |
|--------|-------|---|------------------------|
| ecommerce | 98 | 34.3% | +14.3% (overrepresented) |
| healthcare | 59 | 20.6% | +0.6% |
| analytics | 48 | 16.8% | −3.2% |
| hr | 42 | 14.7% | −5.3% |
| inventory | 39 | 13.6% | −6.4% (underrepresented) |

**Domain × Difficulty cross-tabulation:**

| Domain | Easy | Medium | Hard | Total |
|--------|------|--------|------|-------|
| ecommerce | 52 | 32 | 14 | **98** |
| healthcare | 30 | 18 | 11 | **59** |
| analytics | 24 | 13 | 11 | **48** |
| hr | 17 | 15 | 10 | **42** |
| inventory | 19 | 12 | 8 | **39** |

All domains have non-zero representation at all difficulty levels — no structural gaps.

### 3.4 Difficulty Calibration

| Difficulty | Count | Avg SQL tokens | Min | Max |
|------------|-------|----------------|-----|-----|
| easy | 142 | **10.0** | 5 | 29 |
| medium | 90 | **25.5** | 8 | 48 |
| hard | 54 | **35.8** | 12 | 82 |

SQL length increases monotonically (easy < medium < hard). Query type × difficulty cross-tab shows logically consistent assignment:

| Query Type | Easy | Medium | Hard |
|------------|------|--------|------|
| aggregate | 88 | 36 | 6 |
| select | 48 | 6 | 0 |
| join | 6 | 46 | 4 |
| subquery | 0 | 2 | 23 |
| window | 0 | 0 | 12 |
| cte | 0 | 0 | 9 |

`SELECT` is easy-only; `subquery`, `window`, and `cte` are concentrated in hard. This structure is semantically sound.

### 3.5 Composite Coverage Score

| Dimension | Score | Basis |
|-----------|-------|-------|
| Deduplication | **0.99** | Near-duplicate rate 0.5% |
| Domain Balance | **0.96** | Shannon entropy / H_max |
| Feature Coverage | **0.71** | 7/10 target clauses ≥5% |
| Phrasing Diversity | **0.67** | 6/9 target starters covered |
| **Composite (mean)** | **0.78** | — |

---

## 4. Key Findings

**F1 — Difficulty is well-calibrated.** SQL token count increases monotonically: 10.0 → 25.5 → 35.8. Query type assignments are logically consistent (SELECT → easy, CTE/window → hard). This is the strongest quality signal in the dataset.

**F2 — Domain balance is near-ideal.** Shannon balance of 0.96 confirms all five domains are meaningfully represented at all difficulty levels. The only outlier is ecommerce at 34.3%.

**F3 — Deduplication is effective.** Zero exact duplicates. Near-duplicate rate (Jaccard > 0.7) of 0.5% across 1,000 random pairs — effectively zero given the domain-specific short vocabulary.

**F4 — Question phrasing is concentrated.** Four starters (what/find/how/list) cover 80.3% of questions. Models fine-tuned on this data may underperform on imperative commands ("calculate", "retrieve") or declarative framings ("the total revenue is...").

**F5 — Aggregate function gaps exist.** MAX (1.0%), MIN (1.7%), and COALESCE (0.0%) are severely underrepresented relative to COUNT (30.4%) and SUM (18.2%). This could cause fine-tuned models to default to COUNT/SUM even when MAX/MIN is correct.

**F6 — Complex patterns are sparse.** CTEs (3.1%, n=9) and window functions (4.2%, n=12) are underrepresented for their practical importance in enterprise SQL. LEFT JOIN (2.8%) and CASE (1.4%) are also gap areas.

---

## 5. Limitations

1. **No execution validation.** SQL correctness is assessed structurally (keyword presence, token counts), not by running against a populated database. Syntactically correct queries may produce incorrect results.

2. **Jaccard as near-duplicate metric.** Jaccard similarity on token sets misses semantic similarity. Two questions phrased differently but meaning the same thing count as distinct even when the SQL is identical.

3. **Token-length as complexity proxy.** SQL token count correlates with complexity but doesn't capture semantic depth (e.g., a short correlated subquery may be harder than a long flat aggregate).

4. **Distinct-N interpretation.** Distinct-1 of 0.157 should not be interpreted as a quality problem. For domain-specific questions averaging 8.8 tokens, vocabulary reuse is expected. Contextualize against the near-duplicate rate (0.5%).

5. **Informal coverage score.** The Composite Coverage Score is a heuristic constructed for this analysis. It is not a validated benchmark metric and should not be used for cross-dataset comparisons without re-grounding the sub-scores.

---

## 6. Recommendations for v2

**Short term (immediate):**
- Add 10–15 examples each for `MAX`, `MIN`, `COALESCE`
- Add question starters: *calculate, retrieve, identify, compare, determine*
- Balance inventory domain toward 55 examples (currently 39)

**Medium term:**
- Expand CTEs to 25+ examples across all domains
- Add `LEFT JOIN`, `FULL OUTER JOIN`, and `CASE WHEN` patterns
- Add multi-condition `HAVING` clauses

**Long term:**
- Execution-validate all queries against DuckDB/SQLite with synthetic data
- Generate 3–5 paraphrase variants per SQL query
- Scale to 1,000+ examples with v2 tag

---

## References

- [Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task](https://arxiv.org/abs/1809.08887) — Yu et al., 2018
- [WikiSQL: A Large Annotated Semantic Parsing Corpus for Developing Natural Language Interfaces to Relational Databases](https://arxiv.org/abs/1709.00103) — Zhong et al., 2017
- [BIRD: A Big Bench for Large-Scale Database Grounded Text-to-SQLs](https://bird-bench.github.io/) — Li et al., 2023
- [A Survey on Text-to-SQL Parsing: Concepts, Methods, and Future Directions](https://arxiv.org/abs/2208.13629) — Qin et al., 2022
- [Distinct-N metric for text diversity](https://arxiv.org/abs/1510.03055) — Li et al., 2016

---

## Citation

```bibtex
@techreport{saluteface_sql_coverage_2026,
  title  = {Coverage Analysis: SQL Natural Language Pairs Synthetic Benchmark},
  author = {SaluteFace AAC},
  year   = {2026},
  month  = {10},
  note   = {Technical report. Not peer reviewed. Computed on SalutefaceAAC/sql-natural-language-pairs v1.0.},
  url    = {https://huggingface.co/datasets/SalutefaceAAC/sql-dataset-coverage-analysis}
}
```

---

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