toolevalxm/SuperQA-Dataset-TestRepo

SuperQA-Dataset

---

1. Introduction

The SuperQA-Dataset represents a major advancement in question-answering benchmark datasets. In this latest release, we have significantly improved data quality through enhanced curation pipelines, rigorous validation processes, and comprehensive quality assurance measures. The dataset demonstrates exceptional performance across various data quality metrics, making it ideal for training and evaluating state-of-the-art QA models.

Compared to previous versions, the upgraded dataset shows significant improvements in data quality. For instance, in terms of completeness, the dataset has improved from 85% in the previous version to 96.5% in the current version. This advancement stems from enhanced data collection methodologies: our new pipeline processes 3x more source documents while maintaining stricter quality thresholds.

Beyond improved completeness, this version also offers reduced noise, better answer coverage, and enhanced metadata annotations.

2. Quality Assessment Results

Comprehensive Quality Metrics

	Metric	Dataset-A	Dataset-B	Dataset-C	SuperQA-Dataset
Core Quality Metrics	Completeness	0.845	0.867	0.882	0.965
	Accuracy	0.891	0.903	0.912	0.972
	Consistency	0.823	0.841	0.856	0.948
Temporal & Validity	Timeliness	0.756	0.778	0.792	0.891
	Validity	0.812	0.835	0.848	0.938
	Uniqueness	0.934	0.942	0.951	0.987
	Integrity	0.867	0.889	0.901	0.961
Relevance & Access	Relevance	0.723	0.745	0.762	0.856
	Accessibility	0.912	0.925	0.934	0.978
	Conformity	0.834	0.851	0.867	0.945
	Precision	0.878	0.895	0.908	0.968
Advanced Quality	Traceability	0.689	0.712	0.731	0.823
	Representativeness	0.756	0.778	0.795	0.889
	Portability	0.823	0.845	0.861	0.941
	Credibility	0.901	0.918	0.932	0.981

Overall Quality Summary

The SuperQA-Dataset demonstrates exceptional quality across all evaluated metric categories, with particularly notable results in accuracy, uniqueness, and credibility metrics.

3. Dataset Explorer & API

We offer a dataset explorer and API for you to interact with SuperQA-Dataset. Please check our official website for more details.

4. How to Use

Please refer to our code repository for more information about using SuperQA-Dataset.

Loading the Dataset

from datasets import load_dataset

dataset = load_dataset("your-username/SuperQA-Dataset-TestRepo")

Dataset Structure

The dataset contains the following splits:

• train: Training examples
• validation: Validation examples
• test: Test examples

Each example contains:

• question: The question text
• context: Relevant context passage
• answer: The correct answer
• metadata: Additional annotations

Recommended Usage

We recommend using this dataset with the following settings:

1. Use stratified sampling for training
2. Apply data augmentation carefully
3. Validate model outputs against the provided answer formats

Data Format

For loading, please follow the standard Hugging Face datasets format:

from datasets import load_dataset

# Load specific split
train_data = load_dataset("your-username/SuperQA-Dataset-TestRepo", split="train")

# Access examples
for example in train_data:
    question = example["question"]
    answer = example["answer"]

5. License

This dataset is licensed under the Apache 2.0 License. The use of SuperQA-Dataset is also subject to the Apache 2.0 License. The dataset supports commercial use and derivative works.

6. Contact

If you have any questions, please raise an issue on our GitHub repository or contact us at data@superqa-dataset.ai.