Update model info

README.md

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----
-license: mit
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+---
+license: mit
+tags:
+- code
+- link
+- urlshortener
+---
+
+# Model Card for AI-URL-Shortener
+
+<!-- Provide a quick summary of what the model is/does. -->
+Model Name: AI-URL-Shortener
+
+## Model Details
+
+### Model Description
+
+<!-- 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.
+
+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.
+
+Metadata:
+- **Developed by:** LinksGPT Team
+- **Model type:** LLM
+- **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. -->
+Intended Users:
+- URL shortening platforms.
+- Marketers looking for brand-aligned short links.
+- Developers integrating custom URL shorteners into applications.
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+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
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+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.
+
+### 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.
+
+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
+
+### 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 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.
+
+## 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:
+- 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.
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+## Technical Specifications
+
+### Model Architecture and Objective
+
+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
+
+#### Software
+
+[More Information Needed]
+
+## More About LinksGPT
+
+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
+
+LinksGPT
+
+## Model Card Contact
+
+service@linksgpt.com