Mastering Word Embeddings in 10 Minutes with TensorFlow Covering the Basics of Word Embedding, One Hot Encoding, Text Ve

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.

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:

“Well, I was lucky that I got started early,” admitted Buffett, as he hinted at how he mastered this success formula. “My dad happened to be in the investment business, so I would go down to his office on Saturdays.”

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he United States economy is set to fall by 4.3% this year, but the economic contractions in the UK, France, Italy and Spain are around 10%. With lockdowns returning in Europe due to the pandemic, the decline in GDP is far from over.

After a few seconds of groping with the DNA of both his and Buffett’s line of thinking, Jay-Z noted: “And that’s the key to being a recording artist. You’re telling your story or finding your truth at the moment.”

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:

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.

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.

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:

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

Find out what you do best; then, find out how you can pay most of your attention to doing it; then, get someone to pay you for having paid most of your attention to mastering that one thing.

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:

There’s a whole bunch of things I don’t know a thing about. I just stay away from those. I stay within what I call my circle of competence. Tom Watson [IBM founder] said it best. He said, “I’m no genius, but I’m smart in spots, and I stay around those spots.” (source)

Jay-Z’s ears perked up. And when Buffett seconds later said, “That’s a little bit like these rules I have. The first rule is don’t lose, and the second rule is never forget the first rule,” Jay-Z’s eyes lit up. After all, since day one his motto has been, “I will not lose, for even in defeat there’s a valuable lesson learned — so that evens it up for me.”

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.