Interpretability is a desirable property in deep neural network solutions until you need to sacrifice other aspects such

Interpretability is one of the most challenging aspects of the deep learning space. Imagine understanding a neural network with hundreds of thousands of neurons distributed across thousands of hidden layers. The interconnected and complex nature of most deep neural networks makes unsuitable for traditional debugging tools. As a result, data scientists often rely on visualization techniques that help them understand how neural networks make decisions which becomes an constant challenge. To advance this area, OpenAI just unveiled Microscope and the Lucid Library which enable the visualization of neurons within a neural network.

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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:

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Mastering Word Embeddings in 10 Minutes with TensorFlow Covering the Basics of Word Embedding, One Hot Encoding, Text Vectorization, Embedding Layers, and an Example Neural…towardsdatascience.com

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g models are not able to understand the difference between e.g. units, and will naturally apply more weight to features of high magnitudes. This can destroy an algorithm’s ability to predict new data points well. Further, training machine learning models with features of high magnitude will be slower than necessary, at least if gradient descent is used. This is because gradient descent converges faster when the input values are in approximately the same range.,

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:

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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.

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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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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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Since you are reading this article, I am sure that we share similar interests and are/will be in similar industries. So let’s connect via Linkedin! Please do not hesitate to send a contact request! Orhan G. Yalçın — Linkedin

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Mastering Word Embeddings in 10 Minutes with IMDB Reviews Learn the Basics of Text Vectorization, Create a Word Embedding Model trained with a Neural Network on IMDB Reviews…towardsdatascience.com

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IMDB Reviews Dataset is a large movie review dataset collected and prepared by Andrew L. Maas from the popular movie rating service, IMDB. The IMDB Reviews dataset is used for binary sentiment classification, whether a review is positive or negative. It contains 25,000 movie reviews for training and 25,000 for testing. All these 50,000 reviews are labeled data that may be used for supervised deep learning. Besides, there is an additional 50,000 unlabeled reviews that we will not use in this case study. In this case study, we will only use the training dataset.

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Mastering Word Embeddings in 10 Minutes with IMDB Reviews Learn the Basics of Text Vectorization, Create a Word Embedding Model trained with a Neural Network on IMDB Reviews…towardsdatascience.com

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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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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.

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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.