In this article, I’m going to take you through an in-depth review of BERT in Machine Learning for word embeddings produced by Google for Machine Learning. Here I’ll show you how to get started with BERT in Machine Learning by producing your word embeddings.

What is BERT in Machine Learning?

BERT stands for Bidirectional Encoder Representations from Transformers, BERT in Machine Learning are models for pre-trained language representations that can be used to create models for the tasks of Natural Language Processing.

You can either use these models to extract high-quality language functionality from your text data, or you can refine these models on specific tasks such as classification, feature recognition, answering questions, etc. with your data to produce a state of artistic predictions.

Why BERT Embeddings for NLP?

First, the BERT embeddings are very useful for keyword expansion, semantic search, and other information retrievals. For example, if you want to match customer questions or research to previously answered questions or well-researched research, these representations will help you accurately retrieve results that match customer intent and contextual meaning, even in the absence of overlapping keywords or phrases.

Secondly, and perhaps the most important reason is that these vectors can be used as high-quality features inputs in the downstream models. NLP models such as LSTMs or CNNs require inputs in the form of digital vectors, which typically means translating features such as vocabulary and parts of speech into digital representations.

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