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.

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.

Metric Name	Description	Optimization Direction
train:mean_rho	The mean rho (Spearman’s rank correlation coefficient) on WS-353 word similarity datasets	Maximize
validation:accuracy	The classification accuracy on the user-specified validation dataset	Maximize

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.

Parameter Name	Parameter Type	Recommended Ranges or Values
batch_size	`IntegerParameterRange`	[8-32]
epochs	`IntegerParameterRange`	[5-15]
learning_rate	`ContinuousParameterRange`	MinValue: 0.005, MaxValue: 0.01
min_count	`IntegerParameterRange`	[0-100]
mode	`CategoricalParameterRange`	[`'batch_skipgram'`, `'skipgram'`, `'cbow'`]
negative_samples	`IntegerParameterRange`	[5-25]
sampling_threshold	`ContinuousParameterRange`	MinValue: 0.0001, MaxValue: 0.001
vector_dim	`IntegerParameterRange`	[32-300]
window_size	`IntegerParameterRange`	[1-10]

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

Parameter Name	Parameter Type	Recommended Ranges or Values
buckets	`IntegerParameterRange`	[1000000-10000000]
epochs	`IntegerParameterRange`	[5-15]
learning_rate	`ContinuousParameterRange`	MinValue: 0.005, MaxValue: 0.01
min_count	`IntegerParameterRange`	[0-100]
mode	`CategoricalParameterRange`	[`'supervised'`]
vector_dim	`IntegerParameterRange`	[32-300]
word_ngrams	`IntegerParameterRange`	[1-3]