Using BERT with Pytorch

A super-easy practical guide to build you own fine tuned BERT based architecture using Pytorch.

If you understand BERT you might identify you will need to take these two steps in your code: tokenize the samples and build your own fine-tuned architecture.

BERT uses a special tokenization (BPE) of the words. In addition, depending on your task, each sentence can be padded with [CLS] at the beginning of the first sentence and [SEP] tokens at the end of each sentence.
The [CLS] token is used mainly for classification tasks, and the [SEP] token is used for multiple sentences for tasks such as SNLI or question answering.

2. Build your own architecture based on BERT

Unlike traditional embeddings, BERT embeddings are context related, therefore we need to rely on a pretrained BERT architecture. In full sentence classification tasks we add a classification layer on top of the output for the [CLS] token. In sequence tagging we will need the full output of the sequence. This simple example is a sequence tagging one.

3. How it all comes together

I hope this makes working with pre-trained BERT model in Pytorch easier.

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