aselasperera/CAD-SResumeCredibilityAssessmentDataset

---
license: cc-by-4.0
task_categories:
- text-classification
language:
- en
tags:
- HR
- Technical
- Resume
pretty_name: CAD-S
size_categories:
- 10K<n<100K
---

# CAD-S: Resume Credibility Assessment Dataset

## 📌 Overview
CAD-S (Credibility Assessment Dataset - Synthetic) is the first openly available dataset designed specifically for **resume credibility assessment** using Natural Language Processing techniques.

The dataset supports supervised learning for detecting inconsistencies between claimed skills and supporting evidence (e.g., projects, experience) within resumes.

This dataset is intended for:
- Resume verification systems
- Natural Language Inference (NLI) tasks
- AI-assisted recruitment tools
- Research in credibility assessment and fraud detection

---

## 🎯 Task Definition
CAD-S frames resume verification as a **Natural Language Inference (NLI)** problem, where:

- **Premise** → Claimed skill or statement
- **Hypothesis** → Supporting evidence from the resume

The model predicts the logical relationship:

- **Entailment** → Evidence supports the claim
- **Contradiction** → Evidence conflicts with the claim
- **Neutral** → No clear relationship

---

## 📊 Dataset Summary

- **Total Resumes:** 10,100
- **Format:** CSV
- **Language:** English
- **Domain:** Technical / IT resumes
- **Experience Level:** 0–5 years (students & early-career professionals)

---

## 📄 Data Format

Each row represents a labeled inference pair:

| Column Name | Description |
|------------|-------------|
| resume_id | Unique identifier for each resume |
| premise | Claimed skill or statement |
| hypothesis | Supporting project or experience description |
| label | Entailment / Contradiction / Neutral |

### Example

```csv
resume_id,premise,hypothesis,label
1,"Proficient in Python","Developed a machine learning model using Python",entailment
2,"Expert in Java","No projects related to Java listed",contradiction