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Chapter 14Figure 51: AutoML Text Classification – summary of label distributionFigure 52: AutoML Text Classification – training a new model[ 527 ]

An introduction to AutoMLAt the end, the model is built and it achieves a good precision of 87.6% and recall of84.1% (see Figure 53):Figure 53: AutoML Text Classification – precision and recallIf you are interested in playing some more with happiness-related datasets, I suggesthaving a look at Kaggle: https://www.kaggle.com/ritresearch/happydb.Using Cloud AutoML ‒ Translation solutionIn this solution, we are going to auto-create a model for translating text fromEnglish to Spanish built on the top of a large model provided by Google as the base.As usual, the first step is to activate the solution (see Figure 54) and then create adataset (see Figure 55):Figure 54: AutoML Text Translation – accessing the solution[ 528 ]

Chapter 14

Figure 51: AutoML Text Classification – summary of label distribution

Figure 52: AutoML Text Classification – training a new model

[ 527 ]

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