README.md

---
license: cc-by-nc-4.0  
language:
  - en
task_categories:
  - text2text-generation
tags:
  - vehicle-diagnostics
  - automotive
  - telemetry
  - predictive-maintenance
  - industrial-ai
pretty_name: Vehicle Diagnostic Sample Dataset
size_categories:
  - n<100
---

# Vehicle Diagnostic Sample Dataset

## 🧩 Dataset Summary

This dataset contains a sample subset of structured vehicle diagnostic logs generated for various vehicle types and subsystems, such as **transmissions**, **battery systems**, **brakes**, and **engines**. Each entry includes detailed parameters such as fault codes, performance metrics, measurements, temporal trends, and maintenance recommendations.

This subset (500 examples) is meant to demonstrate the structure and potential use cases of the **full dataset** available commercially on [Gumroad](https://datadeveloper1.gumroad.com/l/oizcli) for industrial, machine learning, and predictive maintenance applications.

## 💡 Use Cases

- Train models for **fault prediction** and **diagnosis generation**
- Fine-tune text-to-text models on structured industrial reports
- Build synthetic data generators for simulation platforms
- Analyze parameter trends for **telemetry-driven maintenance planning**

## 📁 Dataset Structure

Each entry follows the structure:

```json
{
  "input": "Generate comprehensive vehicle diagnostic for <vehicle> <system> system, config=<config>",
  "output": "<structured diagnostic report with parameters, metrics, fault code, and recommendations>"
}
🧪 Example

{
  "input": "Generate comprehensive vehicle diagnostic for commercial transmission system, config=automatic",
  "output": "Diagnostic ID=17f869b8... torque=261.78 Nm... Maintenance Recommendations: filter replacement, fluid change, seal inspection"
}
📥 Loading the Dataset in Python

from datasets import load_dataset

dataset = load_dataset("cjjones/vehicle-diagnostic-sample")
print(dataset["train"][0])