Showing 4 open source projects for "level set methods"

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    Datasets

    Datasets

    Hub of ready-to-use datasets for ML models

    Datasets is a library for easily accessing and sharing datasets, and evaluation metrics for Natural Language Processing (NLP), computer vision, and audio tasks. Load a dataset in a single line of code, and use our powerful data processing methods to quickly get your dataset ready for training in a deep learning model. Backed by the Apache Arrow format, process large datasets with zero-copy reads without any memory constraints for optimal speed and efficiency. We also feature a deep...
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    LexiFinder

    LexiFinder

    AI-powered semantic indexing: automating the creation of book indexes

    LexiFinder is a tool to generate analytic indexes from documents automatically. Given one or more source documents and a set of keywords, it extracts all nouns, compares them semantically to the keywords using a pretrained NLP model, and produces a structured, hierarchical index ready to be included in a book or manuscript. LexiFinder works in two ways: as a command-line tool for scripting, automation, and batch processing, and as a graphical application for a guided, point-and-click...
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  • 3
    NLP Best Practices

    NLP Best Practices

    Natural Language Processing Best Practices & Examples

    ...The goal of this repository is to build a comprehensive set of tools and examples that leverage recent advances in NLP algorithms, neural architectures, and distributed machine learning systems.
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  • 4
    GT NLP Class

    GT NLP Class

    Course materials for Georgia Tech CS 4650 and 7650

    This repository contains lecture notes, slides, assignments, and code for a university-level Natural Language Processing course. It spans core NLP topics such as language modeling, sequence tagging, parsing, semantics, and discourse, alongside modern machine learning methods used to solve them. Students work through programming exercises and problem sets that build intuition for both classical algorithms (like HMMs and CRFs) and neural approaches (like word embeddings and sequence models). ...
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