Training the model might take a while, so ensure you enabled the GPU acceleration from the Notebook Settings. After our

Additionally, I believe I should mention that although Open AI’s GPT3 outperforms BERT, the limited access to GPT3 forces us to use BERT. But rest assured, BERT is also an excellent NLP model. Here is a basic visual network comparison among rival NLP models: BERT, GPT, and ELMo:

Steve Forbes, thankfully, sensed the need to probe deeper. “But what was that extra thing?” Forbes asked, so as crack the door open for Buffett to reveal the extra- he added to the ordinary investor, which lead to his extra-ordinary compounded returns.

So, I don’t want to dive deep into BERT since we need a whole different post for that. In fact, I already scheduled a post aimed at comparing rival pre-trained NLP models. But, you will have to wait for a bit.

4 Reasons Why You Should Use Google Colab for Your Next Project Learn whether iPython, Jupyter Notebook, and Google Colab are Rivals or Complimentary Tools; Understand Their…towardsdatascience.com

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o get her a phone. This is all my fault. But then I wonder if it wasn’t inevitable. She was never going to make plays with puppets or sew clothes for dolls forever. When friends visit, she occasionally asks them to watch her latest TikTok videos. Some are even funny and creative. Or so I tell myself. It helps with the guilt.

One of the questions that I had the most difficulty resolving was to figure out where to find the BERT model that I can use with TensorFlow. Finally, I discovered Hugging Face’s Transformers library.

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.

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.

BERT stands for Bidirectional Encoder Representations from Transformers and it is a state-of-the-art machine learning model used for NLP tasks. Jacob Devlin and his colleagues developed BERT at Google in 2018. Devlin and his colleagues trained the BERT on English Wikipedia (2,500M words) and BooksCorpus (800M words) and achieved the best accuracies for some of the NLP tasks in 2018. There are two pre-trained general BERT variations: The base model is a 12-layer, 768-hidden, 12-heads, 110M parameter neural network architecture, whereas the large model is a 24-layer, 1024-hidden, 16-heads, 340M parameter neural network architecture. Figure 2 shows the visualization of the BERT network created by Devlin et al.

Transformers provides thousands of pretrained models to perform tasks on texts such as classification, information extraction, question answering, summarization, translation, text generation, etc in 100 languages. Its aim is to make cutting-edge NLP easier to use for everyone.

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.

After the installation is completed, we will load the pre-trained BERT Tokenizer and Sequence Classifier as well as InputExample and InputFeatures. Then, we will build our model with the Sequence Classifier and our tokenizer with BERT’s Tokenizer.

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 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:

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: