Showing 9 open source projects for "ofn3-export-layers"

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  • 1
    BertViz

    BertViz

    BertViz: Visualize Attention in NLP Models (BERT, GPT2, BART, etc.)

    ...The head view visualizes attention for one or more attention heads in the same layer. It is based on the excellent Tensor2Tensor visualization tool. The model view shows a bird's-eye view of attention across all layers and heads. The neuron view visualizes individual neurons in the query and key vectors and shows how they are used to compute attention.
    Downloads: 0 This Week
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  • 2
    Generative AI Swift

    Generative AI Swift

    This SDK is now deprecated, use the unified Firebase SDK

    ...Despite its deprecated label, the repo remains valuable for developers who want to see how a native Swift integration might be structured before migrating to newer SDKs. Maintainability is emphasized: modular layers separate networking, prompt handling, and UI logic, making adaptation easier when switching to updated APIs.
    Downloads: 0 This Week
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  • 3
    ChatGPT Desktop Application

    ChatGPT Desktop Application

    đź”® ChatGPT Desktop Application (Mac, Windows and Linux)

    ChatGPT Desktop Application (Mac, Windows and Linux)
    Downloads: 128 This Week
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  • 4
    ChatGPT Console Client in Golang

    ChatGPT Console Client in Golang

    ChatGPT Console client in Golang

    chatgpt: Chat GPT console client in Golang. A Golang console client for ChatGPT using GPT. Request your OpenAPI key.
    Downloads: 0 This Week
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  • 5
    pdf-extractor

    pdf-extractor

    Node.js module for rendering pdf pages to images, svgs and HTML files

    Pdf-extractor is a wrapper around pdf.js to generate images, svgs, html files, text files and json files from a pdf on node.js. A DOM Canvas is used to render and export the graphical layer of the pdf. Canvas exports *.png as a default but can be extended to export to other file types like .jpg. Pdf objects are converted to svg using the SVGGraphics parser of pdf.js. Pdf text is converted to HTML. This can be used as a (transparent) layer over the image to enable text selection. Pdf text is extracted to a text file for different usages (e.g. indexing the text). ...
    Downloads: 0 This Week
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  • 6
    onnxt5

    onnxt5

    Summarization, translation, sentiment-analysis, text-generation, etc.

    ...Other tasks just require to change the prefix in your prompt, for instance for summarization. Run any of the T5 trained tasks in a line (translation, summarization, sentiment analysis, completion, generation) Export your own T5 models to ONNX easily. Utility functions to generate what you need quickly. Up to 4X speedup compared to PyTorch execution for smaller contexts.
    Downloads: 0 This Week
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  • 7
    A Netflix film cover generator Nuxt.js

    A Netflix film cover generator Nuxt.js

    A tool for generating Netflix show image

    We love Netflix, but we love memes even more. We thought that helping Netflix on their UI/UX testing with a tool that can create show images easily with an export function to png. A tool for generating Netflix shows an image. You can visit the demo website hosted on Netlify. This is an open-source tool and it is available on Github. On this tool you have a full editable canvas where you can edit content, text position, text dimension, gradient position and change the background image. In order to change the element position you can just click and drag anywhere. ...
    Downloads: 0 This Week
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  • 8
    FID score for PyTorch

    FID score for PyTorch

    Compute FID scores with PyTorch

    This is a port of the official implementation of Fréchet Inception Distance to PyTorch. FID is a measure of similarity between two datasets of images. It was shown to correlate well with human judgement of visual quality and is most often used to evaluate the quality of samples of Generative Adversarial Networks. FID is calculated by computing the Fréchet distance between two Gaussians fitted to feature representations of the Inception network. The weights and the model are exactly the same...
    Downloads: 4 This Week
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  • 9
    Grenade

    Grenade

    Deep Learning in Haskell

    ...Because the types are so rich, there's no specific term level code required to construct this network; although it is of course possible and easy to construct and deconstruct the networks and layers explicitly oneself. Networks in Grenade can be thought of as a heterogeneous list of layers, where their type includes not only the layers of the network but also the shapes of data that are passed between the layers. To perform back propagation, one can call the eponymous function which takes a network, appropriate input, and target data, and returns the back propagated gradients for the network. ...
    Downloads: 0 This Week
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