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StarsMakeGalaxy/enterprise-text2sql-curated-600

🚀 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… See the full description on the dataset page: https://huggingface.co/datasets/StarsMakeGalaxy/enterprise-text2sql-curated-600.

sourceHugging Faceapache-2.0updated 1mo agoView on Hugging Face
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Dataset Card

🚀 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

SplitRowsProportionPaper Design & Purpose
`train` (Slice 1: Gold CoT)36060.0%Full domain context + verbose 4-phase <Thinking> Chain-of-Thought reasoning (Constraint Analysis, Formal Derivation, Numerical Gating, Implementation).
`train` (Slice 2: Hard Negatives)18030.0%Contrastive edge-case disambiguation & boundary refutations (e.g. Non-existent columns, TLE quadratic overhead, Regulation E APP fraud boundaries).
`train` (Slice 3: Replay Buffer)6010.0%Domain primitives & optimization anchors to prevent catastrophic forgetting.
Total `train` Split600100%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

python
from datasets import load_dataset

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