CoolFace
Datasetpublic

llmsql-bench/llmsql-2.0-fine-tune-ready

LLMSQL Benchmark 2.0 (Finetune-Ready) This benchmark is designed to evaluate text-to-SQL models. For usage of this benchmark see llmsql-bench/llmsql-2.0. This repository contains a finetune-ready version of the LLMSQL benchmark: LLMSQL 2.0 on Hugging Face. The dataset is structured in a messages format suitable for instruction-tuned models, where each example has a messages field. This field is a list of dictionaries with: "role": "user" — the input question or prompt… See the full description on the dataset page: https://huggingface.co/datasets/llmsql-bench/llmsql-2.0-fine-tune-ready.

sourceHugging Facemitupdated 7mo agoView on Hugging Face
0likes549downloads
Dataset Card

LLMSQL Benchmark 2.0 (Finetune-Ready)

This benchmark is designed to evaluate text-to-SQL models. For usage of this benchmark see llmsql-bench/llmsql-2.0.

This repository contains a finetune-ready version of the LLMSQL benchmark: LLMSQL 2.0 on Hugging Face.

The dataset is structured in a messages format suitable for instruction-tuned models, where each example has a messages field. This field is a list of dictionaries with:

  • —"role": "user" — the input question or prompt
  • —"role": "assistant" — the expected SQL query

Files / Subsets

The dataset is organized into three shot-based subsets:

SubsetDescriptionSplits
0shotZero-shot examplestrain, validation, test
1shotOne-shot examplestrain, validation, test
5shotFive-shot examplestrain, validation, test

Each subset folder contains a DatasetDict saved in Hugging Face format, which can be loaded using:

python
from datasets import load_dataset

# Load 1-shot subset
ds = load_dataset("llmsql-bench/llmsql-2.0-fine-tune-ready", name="1shot")
print(ds["train"][0])