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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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AIScraper

This is a Python library for scraping data from websites that use the Amazon Interactive Search (AIS) API. 

Features:

* Easy to use: Simply provide a search query and the library will return a list of product results.
* Flexible: You can customize your scraping by specifying filters, such as price range, brand, and category.
* Efficient: The library uses asynchronous requests to speed up the scraping process.
* Reliable: The library is designed to handle rate limits and other website restrictions.

Installation:

```bash
pip install aiscraper
```

Usage:

```python
from aiscraper import AIScraper

Create an instance of the AIScraper class
scraper = AIScraper()

Perform a search for "laptops"
results = scraper.search("laptops")

Print the product titles
for result in results:
    print(result["title"])
```

Documentation:

https://github.com/aiscraper/aiscraper
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AIScraper This is a Python library for scraping data from websites that use the Amazon Interactive Search (AIS) API. Features: * Easy to use: Simply provide a search query and the library will return a list of product results. * Flexible: You can customize your scraping by specifying filters, such as price range, brand, and category. * Efficient: The library uses asynchronous requests to speed up the scraping process. * Reliable: The library is designed to handle rate limits and other website restrictions. Installation: ```bash pip install aiscraper ``` Usage: ```python from aiscraper import AIScraper Create an instance of the AIScraper class scraper = AIScraper() Perform a search for "laptops" results = scraper.search("laptops") Print the product titles for result in results: print(result["title"]) ``` Documentation: https://github.com/aiscraper/aiscraper

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