Datasets:

StarsMakeGalaxy
/

enterprise-text2sql-curated-600

language:
  - en
license: apache-2.0
task_categories:
  - text-generation
tags:
  - sql
  - text-to-sql
  - enterprise
  - unsloth
  - chatml
  - chain-of-thought
  - postgresql
  - snowflake
  - clickhouse
  - bigquery
  - mysql
size_categories:
  - n<1K
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: test
        path: data/test-*
      - split: regression
        path: data/regression-*

🚀 Enterprise Text-to-SQL & Analytical BI (Verbose CoT Reasoning)

This dataset contains 600 curated training records with in-depth, verbose 4-phase <Thinking> Chain-of-Thought reasoning, 100 frozen evaluation benchmark samples, and 50 frozen regression verification samples formatted in standard ChatML (messages) and Prompt-Target pairs, strictly following the Pioneer / Prometheus research paper 3-slice curriculum design.

📊 Dataset Composition & 3-Slice Breakdown

Split	Rows	Proportion	Paper Design & Purpose
`train` (Slice 1: Gold CoT)	360	60.0%	Full domain context + verbose 4-phase `<Thinking>` Chain-of-Thought reasoning (Constraint Analysis, Formal Derivation, Numerical Gating, Implementation).
`train` (Slice 2: Hard Negatives)	180	30.0%	Contrastive edge-case disambiguation & boundary refutations (e.g. Non-existent columns, TLE quadratic overhead, Regulation E APP fraud boundaries).
`train` (Slice 3: Replay Buffer)	60	10.0%	Domain primitives & optimization anchors to prevent catastrophic forgetting.
Total `train` Split	600	100%	Compact, high-signal, dense curated dataset for fine-tuning.
`test` (Frozen Eval)	100	—	Held-out benchmark with zero training contamination (`eval_leakage = 0`).
`regression` (Baseline)	50	—	Baseline regression validation set.

🛡️ 5-Point Quality Audit (100% Passed)

Uniqueness: 100.0% unique prompts (600 / 600).
Zero Eval Leakage: 0 prompt overlaps across train, test, and regression splits.
Replay Fraction: 10.0% dedicated to optimization and schema resilience.

🚀 Usage with Unsloth / Hugging Face

from datasets import load_dataset

dataset = load_dataset("StarsMakeGalaxy/enterprise-text2sql-curated-600")
print(dataset)