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Wide & Deep TF checkpoint (Base, 128k, v1, AMP)

Wide & Deep TF checkpoint (Base, 128k, v1, AMP)

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Description
Wide & Deep Base TensorFlow checkpoint trained with AMP
Publisher
NVIDIA Deep Learning Examples
Latest Version
20.06.0_amp
Modified
April 4, 2023
Size
769.74 MB

Model Overview

Wide & Deep Recommender model.

Model Architecture

Wide & Deep refers to a class of networks that use the output of two parts working in parallel - wide model and deep model - to make predictions of recommenders. The wide model is a generalized linear model of features together with their transforms. The deep model is a series of 5 hidden MLP layers of 1024 neurons each beginning with a dense embedding of features. The architecture is presented in Figure 1.


Figure 1. The architecture of the Wide & Deep model.

Training

This model was trained using script available on NGC and in GitHub repo.

Dataset

The following datasets were used to train this model:

  • Outbrain - Dataset containing a sample of users’ page views and clicks, as observed on multiple publisher sites in the United States between 14-June-2016 and 28-June-2016. Each viewed page or clicked recommendation is further accompanied by some semantic attributes of those documents.

Performance

Performance numbers for this model are available in NGC.

References

  • Original paper
  • NVIDIA model implementation in NGC
  • NVIDIA model implementation on GitHub

License

This model was trained using open-source software available in Deep Learning Examples repository. For terms of use, please refer to the license of the script and the datasets the model was derived from.