4 projects for "multi-threaded" with 2 filters applied:

  • $300 Free Credits for Your Google Cloud Projects Icon
    $300 Free Credits for Your Google Cloud Projects

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  • Stop Cyber Threats with VM-Series Next-Gen Firewall on Azure Icon
    Stop Cyber Threats with VM-Series Next-Gen Firewall on Azure

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  • 1
    Laravel Translation Manager

    Laravel Translation Manager

    Manage Laravel translation files

    Laravel Translation Manager is a package that adds a web-based UI and database-backed workflow for managing Laravel translation files. Instead of replacing Laravel’s built-in translation system, it imports your lang PHP/JSON files into a database so you can view, search, and edit keys and values from a browser. Once translations are updated, you can export them back to flat files so your app continues to use the standard Laravel translation loader. This workflow is especially helpful when...
    Downloads: 5 This Week
    Last Update:
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  • 2
    UnsupervisedMT

    UnsupervisedMT

    Phrase-Based & Neural Unsupervised Machine Translation

    ...The project also provides scripts to fetch and preprocess monolingual data, learn BPE codes, and train cross-lingual embeddings that bootstrap unsupervised alignment between languages. Beyond the core EMNLP 2018 setup, the codebase exposes additional, optional capabilities such as multi-language training, language model pretraining with shared parameters, and adversarial training.
    Downloads: 4 This Week
    Last Update:
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  • 3
    JInsect
    The JINSECT toolkit is a Java-based toolkit and library that supports and demonstrates the use of n-gram graphs within Natural Language Processing applications, ranging from summarization and summary evaluation to text classification and indexing.
    Downloads: 0 This Week
    Last Update:
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  • 4
    CRFSharp

    CRFSharp

    CRFSharp is a .NET(C#) implementation of Conditional Random Field

    ...Moreover, it has many significant improvement than CRF++, such as totally parallel encoding, optimizing memory usage and so on. Currently, when training corpus, compared with CRF++, CRF# can make full use of multi-core CPUs and only uses very low memory, and memory grow is very smoothly and slowly while amount of training corpus, tags increase. with multi-threads process, CRF# is more suitable for large data and tags training than CRF++ now. For example, in machine with 64GB, CRF# encodes model with more than 4.5 hundred million features quickly.
    Downloads: 0 This Week
    Last Update:
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    Build Agents and Models on One Platform

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