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    ludwig-ai/ludwig
    • About
    • Getting Started
    • User Guide
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    • Examples
    • Developer Guide
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    ludwig-ai/ludwig
    • About
    • Getting Started
    • User Guide
      • Command Line Interface
        • LudwigModel
        • Visualization
        • Supported Formats
        • Data Preprocessing
        • Data Postprocessing
        • Dataset Zoo
      • Distributed Training
      • Hyperparameter Optimization
      • AutoML
      • Visualizations
      • Serving
      • Third-Party Integrations
    • Configuration
        • Input Features
        • Output Features
        • Numerical Features
        • Category Features
        • Binary Features
        • Text Features
        • Image Features
        • Audio Features
        • Sequence Features
        • Set Features
        • Bag Features
        • Date Features
        • H3 Features
        • Time Series Features
        • Vector Features
      • Combiner
      • Preprocessing
      • Training
      • Hyperopt
    • Examples
      • Text Classification
      • Named Entity Recognition Tagging
      • Natural Language Understanding
      • Machine Translation
      • Chit-Chat Dialogue Modeling through Sequence2Sequence
      • Sentiment Analysis
      • Image Classification
      • Image Classification (MNIST)
      • One-shot Learning with Siamese Networks
      • Visual Question Answering
      • Spoken Digit Speech Recognition
      • Speaker Verification
      • Binary Classification (Titanic)
      • Timeseries forecasting
      • Timeseries forecasting (Weather)
      • Movie rating prediction
      • Multi-label classification
      • Multi-Task Learning
      • Simple Regression: Fuel Efficiency Prediction
      • Fraud Detection
    • Developer Guide
      • Codebase Structure
      • Add an Encoder
      • Add a Decoder
      • Add a Feature Type
      • Hyper-parameter Optimization
      • Add an Integration
      • Add an Dataset
      • Style Guidelines and Tests
      • Unit Test Design Guidelines
    • Community
    • FAQ

    Examples

    This section contains several examples of how to build models with Ludwig for a variety of tasks. For each task we show an example dataset and a sample model definition that can be used to train a model from that data.

    In addition to the examples here, on the Ludwig medium publication you can find a three part tutorial on Sentiment Analysis with Ludwig:

    • Part I (Training models from scratch)
    • Part II (Finetuning pretrained models)
    • Part III (Hyperparameter Optimization)
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