---
license: mit
tags:
- code
- url
- expand
- link
---
# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->
Model Name: AI-URL-Expander

## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->
AI-URL-Expander is a machine learning model designed to analyze and expand shortened URLs, revealing the original destination and evaluating potential risks such as phishing, scams, and malicious content. This model empowers developers and security analysts to protect users by identifying unsafe links before they are clicked.
The model integrates seamlessly into security tools, browser extensions, and link management platforms, enhancing URL transparency and safety.

Features:
- URL Parsing and Expansion: Resolve short links to their original destinations.
- Metadata Analysis: Extract information such as page titles, descriptions, and keywords for contextual insights.
- Threat Detection: Evaluate links for potential phishing, malware, and scam indicators.
- AI-Powered Risk Scoring: Generate a risk score for each expanded URL based on a combination of metadata and contextual analysis.

- **Developed by:** LinksGPT Team
- **License:** MIT

### Model Sources

<!-- Provide the basic links for the model. -->

- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

Use Cases:
- URL Expansion: Uncover the original URL from a shortened link.
- Risk Analysis: Detect malicious or risky destinations, including phishing, malware, and scams.
- Security Integration: Incorporate into browser extensions, enterprise security solutions, or link management platforms.

How to Use:
- Input the shortened URL into the model.
- Receive the expanded URL and metadata.
- Analyze the risk score and classification to determine the link’s safety.

```python
from transformers import pipeline  

# Load model  
model = pipeline("text-classification", model="huggingface/ai-url-expander")  

# Input shortened URL  
short_url = "https://bit.ly/3example"  

# Analyze and expand  
results = model(f"Expand and analyze: {short_url}")  
print(results)
```

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

Limitations:
- Dynamic Content: Links with dynamically generated content may not always produce consistent results.
- False Positives/Negatives: Risk scoring may occasionally misclassify benign or malicious links.
- Real-Time Dependency: Requires internet access for URL expansion and metadata retrieval.

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

## Training Details

### Training Data

<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

The model was trained on a diverse dataset of shortened and expanded URLs, including:
- Known safe and malicious links.
- Webpage metadata, content, and classifications.
- Reports on phishing and scam indicators.
The dataset includes contributions from public and proprietary threat intelligence sources, ensuring up-to-date risk evaluation.

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

<!-- This should link to a Dataset Card if possible. -->

[More Information Needed]

#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

[More Information Needed]

#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

The model is evaluated on:
- URL Expansion Accuracy: Ability to resolve shortened URLs to their original destinations.
- Risk Classification: Precision and recall in identifying malicious links.
- User Protection: Reduction in the number of risky links accessed by users.

### Results

[More Information Needed]

#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

[More Information Needed]

## Technical Specifications

### Model Architecture and Objective

The model combines:
- URL Resolution Layer: Uses network calls to expand short URLs and retrieve metadata.
- Natural Language Processing (NLP): Analyzes webpage content, titles, and descriptions for potential threats.
- Threat Classification Module: Employs a fine-tuned transformer model to detect risk patterns based on link behavior and content.

### Compute Infrastructure

[More Information Needed]

#### Hardware

[More Information Needed]

#### Software

[More Information Needed]

## Citation [optional]

## More About URL Expander

[URL Expander ](https://www.linksgpt.com/url-expander) can reveal the final destination of any URL, including shortened links. It allows users to see the original or expanded URL that is hidden behind a link.

## More About LinksGPT

[LinksGPT](https://www.linksgpt.com/) is a professional link management platform for custom short urls, brand building and conversion optimization. It offers intelligent URL shortening and expansion, custom domains, team roles, customizable QR codes, tracking and AI-based in-depth analytics, deep linking, openAPI and enhanced link security. Powered by AI, it provides intelligent insights and recommendations based on user behavior and click patterns, support data-driven brand strategies and marketing decisions.

## Model Card Authors

[LinksGPT](https://www.linksgpt.com/)

## Model Card Contact

service@linksgpt.com