Showing 4 open source projects for "org.json-1.6-20240205.jar(js=n-java)"

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  • 1
    NÜWA - Pytorch

    NÜWA - Pytorch

    Implementation of NÜWA, attention network for text to video synthesis

    Implementation of NÜWA, state of the art attention network for text-to-video synthesis, in Pytorch. It also contains an extension into video and audio generation, using a dual decoder approach. It seems as though a diffusion-based method has taken the new throne for SOTA. However, I will continue on with NUWA, extending it to use multi-headed codes + hierarchical causal transformer. I think that direction is untapped for improving on this line of work. In the paper, they also present a way...
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  • 2
    hebrew-gpt_neo

    hebrew-gpt_neo

    Hebrew text generation models based on EleutherAI's gpt-neo

    Hebrew text generation models based on EleutherAI's gpt-neo. Each was trained on a TPUv3-8 which was made available to me via the TPU Research Cloud Program. The Open Super-large Crawled ALMAnaCH coRpus is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus using the goclassy architecture.
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  • 3
    commit-autosuggestions

    commit-autosuggestions

    A tool that AI automatically recommends commit messages

    This is implementation of CommitBERT: Commit Message Generation Using Pre-Trained Programming Language Model. CommitBERT is accepted in ACL workshop : NLP4Prog. Have you ever hesitated to write a commit message? Now get a commit message from Artificial Intelligence! CodeBERT: A Pre-Trained Model for Programming and Natural Languages introduces a pre-trained model in a combination of Program Language and Natural Language(PL-NL). It also introduces the problem of converting code into natural...
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  • 4
    Reliable Metrics for Generative Models

    Reliable Metrics for Generative Models

    Code base for the precision, recall, density, and coverage metrics

    Reliable Fidelity and Diversity Metrics for Generative Models (ICML 2020). Devising indicative evaluation metrics for the image generation task remains an open problem. The most widely used metric for measuring the similarity between real and generated images has been the Fréchet Inception Distance (FID) score. Because it does not differentiate the fidelity and diversity aspects of the generated images, recent papers have introduced variants of precision and recall metrics to diagnose those...
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