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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 5: Pass data to the preprocessing module and BERT

Home > APEX > Storage > White Papers > Sentiment Analysis using Dell APEX File Storage for AWS and Amazon SageMaker > Step 5: Pass data to the preprocessing module and BERT

Step 5: Pass data to the preprocessing module and BERT

  • Below we pass sample text to the preprocessing model. It must be transformed to numeric IDs before being input to BERT.

    We can also try the preprocessing model on some text to see the output.

    Now we have three outputs from the preprocessing that a BERT model uses. The input is truncated to 128 tokens, and input_type_ids are zero because this is a single sentence.

    Before putting BERT into our model, we can look at its outputs. This returns a map with three important keys: pooled_output, sequence_output and encoder_outputs.