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Other examples for such a use-case include: For this spam classifier example, the labeled dataset would be a list of email messages and a label (“spam” or “not spam” for each message). Which would mean we need a labeled dataset to train such a model. This training process is called Fine-Tuning, and has roots in Semi-supervised Sequence Learning and ULMFiT.įor people not versed in the topic, since we’re talking about classifiers, then we are in the supervised-learning domain of machine learning. To train such a model, you mainly have to train the classifier, with minimal changes happening to the BERT model during the training phase. The most straight-forward way to use BERT is to use it to classify a single piece of text.
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So let’s start by looking at ways you can use BERT before looking at the concepts involved in the model itself. There are a number of concepts one needs to be aware of to properly wrap one’s head around what BERT is.
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You can download the model pre-trained in step 1 (trained on un-annotated data), and only worry about fine-tuning it for step 2.īERT builds on top of a number of clever ideas that have been bubbling up in the NLP community recently – including but not limited to Semi-supervised Sequence Learning (by Andrew Dai and Quoc Le), ELMo (by Matthew Peters and researchers from AI2 and UW CSE), ULMFiT (by fast.ai founder Jeremy Howard and Sebastian Ruder), the OpenAI transformer (by OpenAI researchers Radford, Narasimhan, Salimans, and Sutskever), and the Transformer ( Vaswani et al).