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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 4: Select the BERT preprocessing module

Home > APEX > Storage > White Papers > Sentiment Analysis using Dell APEX File Storage for AWS and Amazon SageMaker > Step 4: Select the BERT preprocessing module

Step 4: Select the BERT preprocessing module

  • A preprocessing model must be selected based on the chosen Bert model. For this example, we are using the model ending with A-2.

    bert_model_name = 'small_bert/bert_en_uncased_L-2_H-128_A-2'

    tfhub_handle_encoder = map_name_to_handle[bert_model_name]

    tfhub_handle_preprocess = map_model_to_preprocess[bert_model_name]

    print(f'BERT model selected           : {tfhub_handle_encoder}')

    print(f'Preprocess model auto-selected: {tfhub_handle_preprocess}')

    The output  displays the selected models.

    BERT model selected           : https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-2_H-128_A-2/1

    Preprocess model auto-selected: https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3