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Shift-Ctrl-F: Semantic Search for the Browser

Find information on a webpage using everyday words.
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Shift-Ctrl-F: Semantic Search for the Browser
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Introduction

Shift-Ctrl-F is an innovative AI-powered browser extension that revolutionizes webpage searching. By leveraging natural language processing and MobileBERT technology, it allows users to find information quickly and efficiently using conversational queries. This open-source tool enhances web browsing experiences by providing intelligent, context-aware search capabilities directly on any webpage.

Feature

Natural Language Search

Shift-Ctrl-F enables users to search webpages using natural language queries, making information retrieval more intuitive and user-friendly.

AI-Powered Results

Utilizing MobileBERT, a fine-tuned model on SQuAD via TensorFlowJS, the extension provides accurate and relevant search results by highlighting pertinent elements on the webpage.

Open-Source Availability

The extension is open-sourced on GitHub, encouraging community contributions and continuous improvement.

Easy-to-Use Interface

Users can access Shift-Ctrl-F through a simple popup interface, activated via the toolbar or a keyboard shortcut (Shift-Ctrl-F).

Free to Use

Shift-Ctrl-F is completely free, with no subscription or payment required.

FAQ

How do I use Shift-Ctrl-F?

  1. Open the Shift-Ctrl-F popup via the toolbar or shortcut (Shift-Ctrl-F).
  2. Enter a natural language query into the search bar.
  3. Press enter and wait for the model to return results.

Does Shift-Ctrl-F work on all webpages?

Shift-Ctrl-F is designed to work on most webpages, but may not function on all due to varying levels of semantic search capabilities.

Is my data safe with Shift-Ctrl-F?

The developer has not provided specific information about data collection or usage. Users should exercise caution and refer to the extension's privacy policy for more details.

What are some helpful tips for using Shift-Ctrl-F?

  • Use natural language queries for the most accurate results.
  • Be cautious when analyzing search results, as deep learning models like BERT may return unpredictable or biased results.

Latest Traffic Insights

  • Monthly Visits

    193.90 M

  • Bounce Rate

    56.27%

  • Pages Per Visit

    2.71

  • Time on Site(s)

    115.91

  • Global Rank

    -

  • Country Rank

    -

Recent Visits

Traffic Sources

  • Social Media:
    0.48%
  • Paid Referrals:
    0.55%
  • Email:
    0.15%
  • Referrals:
    12.81%
  • Search Engines:
    16.21%
  • Direct:
    69.81%
More Data

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CO-STAR Prompt Template
==========================

Task Description
Describe the task you want the AI to perform. Be specific and concise.

Input Prompt
Provide the input prompt that the AI will receive. This can be a question, a statement, or a scenario.

Desired Response
Describe the desired response from the AI. What type of answer or output are you expecting?

Constraints
List any constraints or rules that the AI should follow when generating a response. This can include tone, style, or specific keywords to include or avoid.

Evaluation Metrics
Describe how you will evaluate the AI's response. What metrics or criteria will you use to determine the quality of the response?

Example Output
Provide an example of what a good response from the AI might look like. This can help clarify the task and provide a target for the AI to aim for.
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CO-STAR Prompt Template ========================== Task Description Describe the task you want the AI to perform. Be specific and concise. Input Prompt Provide the input prompt that the AI will receive. This can be a question, a statement, or a scenario. Desired Response Describe the desired response from the AI. What type of answer or output are you expecting? Constraints List any constraints or rules that the AI should follow when generating a response. This can include tone, style, or specific keywords to include or avoid. Evaluation Metrics Describe how you will evaluate the AI's response. What metrics or criteria will you use to determine the quality of the response? Example Output Provide an example of what a good response from the AI might look like. This can help clarify the task and provide a target for the AI to aim for.

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Stability Matrix CivitAI Integration 

This document outlines the integration of Stability Matrix into Civitai. 

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Stability Matrix is a powerful tool for evaluating and comparing different AI models. It provides a comprehensive set of metrics to assess model performance across various tasks, including text generation, image generation, and code generation.

Benefits of Integrating Stability Matrix into Civitai:

* Enhanced Model Discovery: Users can easily identify high-performing models based on their desired tasks and preferences.
* Improved Model Selection: The detailed metrics provided by Stability Matrix allow for informed decision-making when choosing a model for a specific project.
* Community-Driven Evaluation: By leveraging the collective wisdom of the Civitai community, Stability Matrix fosters a transparent and collaborative approach to model evaluation.
* Accelerated Model Development: Developers can use Stability Matrix to track the progress of their models and identify areas for improvement.

Implementation Details:

* Stability Matrix scores will be displayed alongside model descriptions on Civitai.
* Users will be able to filter models based on their Stability Matrix scores.
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Future Enhancements:

* Expand support for additional model types, such as code generation models.
* Implement interactive visualizations of Stability Matrix data.
* Allow users to contribute their own model evaluations to the platform.
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Stability Matrix CivitAI Integration This document outlines the integration of Stability Matrix into Civitai. What is Stability Matrix? Stability Matrix is a powerful tool for evaluating and comparing different AI models. It provides a comprehensive set of metrics to assess model performance across various tasks, including text generation, image generation, and code generation. Benefits of Integrating Stability Matrix into Civitai: * Enhanced Model Discovery: Users can easily identify high-performing models based on their desired tasks and preferences. * Improved Model Selection: The detailed metrics provided by Stability Matrix allow for informed decision-making when choosing a model for a specific project. * Community-Driven Evaluation: By leveraging the collective wisdom of the Civitai community, Stability Matrix fosters a transparent and collaborative approach to model evaluation. * Accelerated Model Development: Developers can use Stability Matrix to track the progress of their models and identify areas for improvement. Implementation Details: * Stability Matrix scores will be displayed alongside model descriptions on Civitai. * Users will be able to filter models based on their Stability Matrix scores. * The integration will initially focus on text generation and image generation models. Future Enhancements: * Expand support for additional model types, such as code generation models. * Implement interactive visualizations of Stability Matrix data. * Allow users to contribute their own model evaluations to the platform.

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