| | --- |
| | license: mit |
| | tags: |
| | - code |
| | - link |
| | - urlshortener |
| | --- |
| | |
| | # Model Card for AI-URL-Shortener |
| |
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| | <!-- Provide a quick summary of what the model is/does. --> |
| | Model Name: AI-URL-Shortener |
| |
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| | ## Model Details |
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| | ### Model Description |
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| | <!-- Provide a longer summary of what this model is. --> |
| | AI-URL-Shortener is a machine learning model designed to automate the process of creating meaningful, human-readable URL shorteners. This model analyzes the original link provided by the user, generates a preview of the content, and suggests multiple unique and relevant suffix options for the shortened URL. |
| | The model is built to integrate seamlessly with URL shortener platforms, like [LinksGPT](https://www.linksgpt.com/), and aims to enhance user experience by providing smart suffix recommendations that align with the content of the original link. |
| |
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| | Features: |
| | - Original URL Analysis: Extract metadata such as title, description, and keywords. |
| | - Dynamic Recommendations: Create suffixes based on the extracted metadata, user input, or custom branding. |
| | - Intelligent Validation: Ensure generated suffixes are unique and valid. |
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|
| | Metadata: |
| | - **Developed by:** LinksGPT Team |
| | - **Model type:** LLM |
| | - **License:** MIT |
| |
|
| | ### Model Sources |
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| | <!-- Provide the basic links for the model. --> |
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| | - **Repository:** [More Information Needed] |
| | - **Paper [optional]:** [More Information Needed] |
| | - **Demo [optional]:** [More Information Needed] |
| |
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| | ## Uses |
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| | <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
| | Intended Users: |
| | - URL shortening platforms. |
| | - Marketers looking for brand-aligned short links. |
| | - Developers integrating custom URL shorteners into applications. |
| |
|
| | ### Direct Use |
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| | <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> |
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| | URL Shortening: Automatically generate short and descriptive URLs for social sharing or branding. |
| | Preview Links: Offer a content preview to help users select relevant suffixes for better engagement. |
| | Custom URL Recommendations: Provide personalized suggestions based on the content and user preferences. |
| |
|
| | ## Bias, Risks, and Limitations |
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| | <!-- This section is meant to convey both technical and sociotechnical limitations. --> |
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| | Limitations: |
| | - Content Preview Accuracy: The preview is dependent on the metadata availability of the original link. |
| | - Suffix Creativity: The model generates suffixes within the constraints of URL standards, which may limit overly creative outputs. |
| | - Real-Time Validation: Requires integration with a live URL shortener backend for uniqueness checks. |
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| | ### Recommendations |
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|
| | <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> |
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| | Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. |
| |
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| | ## How to Get Started with the Model |
| |
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| | Use the code below to get started with the model. |
| |
|
| | How to Use: |
| | - Input the original URL into the model. |
| | - Receive a content preview and a list of recommended short-link suffixes. |
| | - Select or customize a suffix based on the recommendations. |
| | - Use the selected suffix to generate the final shortened URL via the backend system. |
| |
|
| | Example code snippet: |
| | ```python |
| | from transformers import pipeline |
| | |
| | # Load model |
| | model = pipeline("text-generation", model="huggingface/ai-url-shortener") |
| | |
| | # Input original URL |
| | original_url = "https://example.com/interesting-article" |
| | |
| | # Generate suffix recommendations |
| | results = model(f"Generate suffixes for: {original_url}") |
| | print(results) |
| | ``` |
| |
|
| | ## Training Details |
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| | ### Training Data |
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| | <!-- 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 large dataset of URLs, metadata, and user-selected short link patterns. The dataset includes a mix of general, e-commerce, social media, and enterprise links, ensuring versatility across industries. |
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| | ## Evaluation |
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| | <!-- This section describes the evaluation protocols and provides the results. --> |
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| | ### Testing Data, Factors & Metrics |
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| | #### Testing Data |
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|
| | <!-- This should link to a Dataset Card if possible. --> |
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| | [More Information Needed] |
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| | #### Factors |
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|
| | <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> |
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| | [More Information Needed] |
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| | #### Metrics |
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| | <!-- These are the evaluation metrics being used, ideally with a description of why. --> |
| | The model is evaluated on: |
| | - Suffix Relevance: How well the generated suffixes align with the link content. |
| | - Uniqueness: Ensuring no duplicate or conflicting suffixes are generated. |
| | - User Engagement: Improvement in click-through rates (CTR) for suggested short links. |
| |
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| | ### Results |
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| | [More Information Needed] |
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| | #### Summary |
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| | ## Technical Specifications |
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| | ### Model Architecture and Objective |
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| | The model leverages a combination of: |
| | - Natural Language Processing (NLP): To understand and extract relevant metadata from the original link. |
| | - Transformer Models: For generating meaningful and creative suffix recommendations. |
| | - Regex and Validation Layers: To ensure all generated suffixes conform to URL standards and avoid duplication. |
| |
|
| | ### Compute Infrastructure |
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| | #### Software |
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| | [More Information Needed] |
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|
| | ## More About LinksGPT |
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| | LinksGPT 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 |
| |
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| | LinksGPT |
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|
| | ## Model Card Contact |
| |
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| | service@linksgpt.com |
