Tune a BlazingText Model

Automatic model tuning, also known as hyperparameter tuning, finds the best version of a model by running many jobs that test a range of hyperparameters on your dataset. You choose the tunable hyperparameters, a range of values for each, and an objective metric. You choose the objective metric from the metrics that the algorithm computes. Automatic model tuning searches the hyperparameters chosen to find the combination of values that result in the model that optimizes the objective metric.

For more information about model tuning, see Automatic model tuning with SageMaker.

Metrics Computed by the BlazingText Algorithm

The BlazingText Word2Vec algorithm (skipgram, cbow, and batch_skipgram modes) reports on a single metric during training: train:mean_rho. This metric is computed on WS-353 word similarity datasets. When tuning the hyperparameter values for the Word2Vec algorithm, use this metric as the objective.

The BlazingText Text Classification algorithm (supervised mode), also reports on a single metric during training: the validation:accuracy. When tuning the hyperparameter values for the text classification algorithm, use these metrics as the objective.

Tunable BlazingText Hyperparameters

Tunable Hyperparameters for the Word2Vec Algorithm

Tune an Amazon SageMaker BlazingText Word2Vec model with the following hyperparameters. The hyperparameters that have the greatest impact on Word2Vec objective metrics are: mode, learning_rate, window_size, vector_dim, and negative_samples.

Tunable Hyperparameters for the Text Classification Algorithm

Tune an Amazon SageMaker BlazingText text classification model with the following hyperparameters.