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Sentiment Analysis using Dell APEX File Storage for AWS and Amazon SageMaker
Executive summary Solution Architecture Solution Overview Solution Validation
Solution Validation
Step 1: Amazon SageMaker Studio
Step 2: Setup and import the IMDB dataset
Step 3: Create the Training, Testing, and Validation sets
Step 4: Select the BERT preprocessing module
Step 5: Pass data to the preprocessing module and BERT
Step 6: Pass data to the preprocessing module and BERT
Step 7: Training and Evaluation
Step 8: Saving and Re-loading the model.
Step 9: Predictions
Conclusion References

Step 8: Saving and Re-loading the model.

Home > APEX > Storage > White Papers > Sentiment Analysis using Dell APEX File Storage for AWS and Amazon SageMaker > Step 8: Saving and Re-loading the model.

Step 8: Saving and Re-loading the model.

  • We can export the fine-tuned model to Dell APEX File Storage for later use.

    To save the model use the .save method, and to reload the model use the .saved_model.load method.


    Reload the model from Dell APEX File Storage to verify it side by side with the model still in memory. 

    The predictions match.