Amazon SageMaker
Developer Guide

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.

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.

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

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.

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]

Tunable Hyperparameters for the Text Classification Algorithm

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]