[go: up one dir, main page]

Scala Algorithms for ChromeOS

Browse free open source Scala Algorithms for ChromeOS and projects below. Use the toggles on the left to filter open source Scala Algorithms for ChromeOS by OS, license, language, programming language, and project status.

  • MongoDB Atlas runs apps anywhere Icon
    MongoDB Atlas runs apps anywhere

    Deploy in 115+ regions with the modern database for every enterprise.

    MongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
    Start Free
  • Combine Jira and SCM data to improve team performance Icon
    Combine Jira and SCM data to improve team performance

    For engineering leaders who need to foster alignment with the business and streamline their operations for better efficiency and higher productivity

    Jellyfish is the leading Engineering Management Platform, providing complete visibility into engineering organizations, the work they do, and how they operate. By analyzing engineering signals from Git and Jira, qualitative team feedback, and contextual business data from roadmapping, incident response, HR, calendar, and collaboration tools, Jellyfish enables engineering leaders to align engineering decisions with business initiatives and deliver the right software, efficiently, on time. With Jellyfish, engineering leaders can focus their teams on what matters most to the business, driving strategic decisions and delivering results.
    Learn More
  • 1
    TextTeaser

    TextTeaser

    TextTeaser is an automatic summarization algorithm

    textteaser is an automatic text summarization algorithm implemented in Python. It extracts the most important sentences from an article to generate concise summaries that retain the core meaning of the original text. The algorithm uses features such as sentence length, keyword frequency, and position within the document to determine which sentences are most relevant. By combining these features with a simple scoring mechanism, it produces summaries that are both readable and informative. Originally inspired by research and earlier implementations, textteaser provides a lightweight solution for summarization without requiring heavy machine learning models. It is particularly useful for developers, researchers, or content platforms seeking a simple, rule-based approach to article summarization.
    Downloads: 3 This Week
    Last Update:
    See Project
  • Previous
  • You're on page 1
  • Next