You deserve to have good outcomes, to win. You deserve to get
Tags: Babies, Education, Democracy, Ethereum, Health

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.

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.

The ones who preach this message, often rich themselves, never give up their riches. They teach you self-sufficiency because they want to gatekeep the narrative. They want to gatekeep the narrative to keep power — one big pyramid instead of letting us all be at the top of our own.

TensorFlow support in the transformers library came later than that for PyTorch, meaning the majority of articles you read on the topic will show you how to integrate HuggingFace and PyTorch — but not TensorFlow.

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.

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.

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.

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:

If you are stuck on CPU, try out Google Colab — it’s a free, cloud-based notebook service provided by Google. Colab includes a GPU as standard — albeit not a particularly powerful one (but it is free).

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:

Because there are a lot of sentence fragments, these can easily pollute the validation set with near-matches to that in the training set. So, I removed them using drop_duplicates, keeping the first record of each unique SentenceId (the full review, meaning we drop all review segments).

We will be using the HuggingFace transformers library to source our transformer models. A smaller transformer model available to us is DistilBERT — a smaller version of BERT with ~40% of the parameters while maintaining ~95% of the accuracy.

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:

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.

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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 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.

These are pretty great results for such a simple output network. Further fine-tuning, the addition of CNNs, LSTMs, or other more expressive networks may improve our results even further.First, we need to prepare our data for our transformer model. What is done here will depend very much on the data and use-case. We will be taking some clean data and processing it with BERT as a classifier for sentiment analysis.

Alternatively, (although I found this to be detrimental) we can even use BERTs pre-pooled output tensors by swapping out last_hidden_state with pooler_output — but that is for another time.

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.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.

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:

Alternatively, (although I found this to be detrimental) we can even use BERTs pre-pooled output tensors by swapping out last_hidden_state with pooler_output — but that is for another time.