If you are stuck on CPU, try out Google Colab — it’s a free, cloud-based notebook service provided by Google. Colab incl

The flow of information through the BERT classifier model. We have two inputs, input_ids and attention_mask, which feed into BERT. BERT outputs two tensors — of which we use the last_hidden_state tensor and discard the pooler_output tensor.

For every BERT-based transformer model, we need two input layers that match our sequence length. We encoded our inputs to a length of 50 tokens — so we use an input shape of (50,) here:

Note: If training BERT layers too, try Adam optimizer with weight decay — which can help reduce overfitting and improve generalization [1]. I would recommend this article for understanding why.

First, we use the optimizer we all know and love. Next, we use category cross-entry and categorical accuracy for our loss and single metric. Because we have one-hot encoded our outputs, we use Categorical.

Input IDs are simply a set of integers that represent a word, “hello” could be 0, “world” might be 1. But, BERT uses a predefined set of mappings — hence why we loaded our tokenizer using the .from_pretrained method.

If you are wanting to train the transformer parameters further, the final line is not necessary! We choose not to as BERT is already an incredibly well built and fine-tuned model. It would take a very long time to train, so for the likely minuscule performance increase — there’s little justification.

We have our encoded inputs IDs and attention masks, and the initialized BERT model — now, we need to add the additional layers required for inputting the input ID and attention mask arrays and the layers required for classifying the BERT output into sentiment ratings.

Here we pull the outputs from distilbert and use a MaxPooling layer to convert the tensor from 3D to 2D — alternatively, use a 3D network (like convolutional or recurrent neural nets) followed by MaxPooling.

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t Dave Chappelle the superstar, it’s about the fourteen-year-old boy that we were introduced to in the first act. We’re being asked to do our part to help that child get back what was taken from him. What’s refusing to watch a TV show compared to that?

Next up are our classification layers. These will take the output from our BERT model and produce one of our three sentiment labels — there are a lot of ways to do this, but we will keep it simple:

Our model summary shows the two input layers, BERT, and our final classification layers. We have a total of 108M parameters, of which just 100K are trainable because we froze the BERT parameters.

The encode_plus output. We are given a dictionary with output encodings for a single sentence. Included are the input IDs (first) and the attention mask (second). Note that the attention mask tells us to focus on the first three tokens only, ignoring the remaining padding tokens.

Input ID array. This sentence only contained a single word as we can see the [SEP] token (101), a word (5650), and the [CLS] token (102). Following this are 47 [PAD] tokens.

DistilBERT is a good option for anyone working with less compute. Just switch out bert-base-cased for distilbert-base-cased below. We initialize the BERT tokenizer and model like so:

Next up is the attention mask. This mask is simply an array of 0s and 1s where each 1 represents a valid word/input ID, and a 0 represents padding. The encode_plus method outputs both input IDs and the attention mask tensors inside a dictionary: