You have successfully built a transformers network with a pre-trained BERT model and achieved ~95% accuracy on the sentiment analysis of the IMDB reviews dataset! If you are curious about saving your model, I would like to direct you to the Keras Documentation. After all, to efficiently use an API, one must learn how to read and use the documentation.
2 — convert_examples_to_tf_dataset: This function will tokenize the InputExample objects, then create the required input format with the tokenized objects, finally, create an input dataset that we can feed to the model.
We have two pandas Dataframe objects waiting for us to convert them into suitable objects for the BERT model. We will take advantage of the InputExample function that helps us to create sequences from our dataset. The InputExample function can be called as follows:
Now we have our basic train and test datasets, I want to prepare them for our BERT model. To make it more comprehensible, I will create a pandas dataframe from our TensorFlow dataset object. The following code converts our train Dataset object to train pandas dataframe:
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We need to tokenize our reviews with our pre-trained BERT tokenizer. We will then feed these tokenized sequences to our model and run a final softmax layer to get the predictions. We can then use the argmax function to determine whether our sentiment prediction for the review is positive or negative. Finally, we will print out the results with a simple for loop. The following lines do all of these said operations:
Now that we have our data cleaned and prepared, we can create text_dataset_from_directory with the following lines. I want to process the entire data in a single batch. That’s why I selected a very large batch size:
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We will use Adam as our optimizer, CategoricalCrossentropy as our loss function, and SparseCategoricalAccuracy as our accuracy metric. Fine-tuning the model for 2 epochs will give us around 95% accuracy, which is great.
Training the model might take a while, so ensure you enabled the GPU acceleration from the Notebook Settings. After our training is completed, we can move onto making sentiment predictions.
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