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A repository to house model building experiments and tools that are part of the Conversation AI effort.

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ConversationAI Models

This repository is contains example code to train machine learning models for text classification as part of the Conversation AI project.

Outline of the codebase

  • experiments/ contains the ML training framework.
  • annotator-models/ contains a Dawid-Skene implementation for modelling rater quality to produce better annotations.
  • attention-tutorial/ contains an introductory ipython notebook for RNNs with attention, as presented at Devoxx talk "Tensorflow, deep learning and modern RNN architectures, without a PhD by Martin Gorner"
  • kaggle-classification/ early experiments with Keras and Estimator for training on the Jigsaw Toxicity Kaggle competition. Will be superceeded by experiments/ shortly.
  • model_evaluation/ contains utilities to use a model deployed on cloud MLE, and some notebooks to illustrate typical evaluation metrics.

About this code

This repository contains example code to help experiment with models to improve conversations; it is not an official Google product.

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A repository to house model building experiments and tools that are part of the Conversation AI effort.

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  • Python 63.0%
  • Jupyter Notebook 30.1%
  • Shell 6.9%