Showing 7 open source projects for "data access layer"

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  • 1
    NVIDIA NeMo

    NVIDIA NeMo

    Toolkit for conversational AI

    NVIDIA NeMo, part of the NVIDIA AI platform, is a toolkit for building new state-of-the-art conversational AI models. NeMo has separate collections for Automatic Speech Recognition (ASR), Natural Language Processing (NLP), and Text-to-Speech (TTS) models. Each collection consists of prebuilt modules that include everything needed to train on your data. Every module can easily be customized, extended, and composed to create new conversational AI model architectures. Conversational AI...
    Downloads: 3 This Week
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  • 2

    modnlp

    Modular Suite of NLP Tools

    modnlp aims to provide a modular architecture and tools for natural language processing written (mainly) in Java. It provides an API and tools for (inverted) indexing, storage and retrieval of large amounts of text, with (XML-based) handling of meta-data, tools for text categorisation, including, functionality for XML parsing, term set reduction (and basic keyword extraction), probabilistic classifier induction, sample classification tools, and evaluation modules, a suite of corpus management, curation and distributed access tools. If you use the tool please consider referencing it using the following article: Luz, S., & Sheehan, S. (2020). ...
    Downloads: 2 This Week
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  • 3
    TXM

    TXM

    Unicode XML TEI text analysis platform

    TXM is a free and open-source cross-platform Unicode & XML based text analysis environment and graphical client, supporting Windows, Linux and Mac OS X. It can also be used online as a J2EE standard compliant web portal (GWT based) with access control built in. DOWNLOAD LATEST VERSION OF TXM : http://textometrie.ens-lyon.fr/spip.php?rubrique61&lang=en TXM offers a comprehensive range of analysis tools (concordances, collocate search, frequency lists, etc.) based on the powerfull CQP...
    Downloads: 9 This Week
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  • 4
    Graph4NLP

    Graph4NLP

    Graph4nlp is the library for the easy use of Graph Neural Networks

    ...Graph4NLP consists of four different layers: 1) Data Layer, 2) Module Layer, 3) Model Layer, and 4) Application Layer. Graph4nlp aims to make it incredibly easy to use GNNs in NLP tasks (check out Graph4NLP Documentation).
    Downloads: 0 This Week
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  • 5
    NLP Best Practices

    NLP Best Practices

    Natural Language Processing Best Practices & Examples

    In recent years, natural language processing (NLP) has seen quick growth in quality and usability, and this has helped to drive business adoption of artificial intelligence (AI) solutions. In the last few years, researchers have been applying newer deep learning methods to NLP. Data scientists started moving from traditional methods to state-of-the-art (SOTA) deep neural network (DNN) algorithms which use language models pretrained on large text corpora. This repository contains examples and...
    Downloads: 0 This Week
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  • 6
    Rasa-UI

    Rasa-UI

    Rasa UI is a frontend for the Rasa Framework

    Rasa UI is a web application built on top of, and for Rasa. Rasa UI provides a web application to quickly and easily be able to create and manage bots, NLU components (Regex, Examples, Entities, Intents, etc.) and Core components (Stories, Actions, Responses, etc.) through a web interface. It also provides some convenience features for Rasa, like training and loading your models, monitoring usage or viewing logs.
    Downloads: 1 This Week
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  • 7
    Seq2seq Chatbot for Keras

    Seq2seq Chatbot for Keras

    This repository contains a new generative model of chatbot

    ...The architecture presented here assumes the same prior distributions for input and output words. Therefore, it shares an embedding layer (Glove pre-trained word embedding) between the encoding and decoding processes through the adoption of a new model.
    Downloads: 0 This Week
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