In this article, I aim to provide a straightforward explanation of BERT, breaking it down to its simplest form. Starting from the very basics, my goal is to make the concept easily digestible and accessible to all.
Think of BERT as a linguistic robot; it possesses the ability to understand our language and engage in meaningful conversations. The acronym BERT stands for “Bidirectional Encoder Representation Transformer,” and believe it or not, this full name contains all the key insights you need to grasp the BERT model.
Let’s begin with the term “Bidirectional.” BERT has a remarkable capability — it can understand and process language from both directions: from left to right and from right to left.
Next up is “Encoder.” Think of it as a tool that can encode or tokenize data for precise and secure processing. Tokenization is the process of converting text into numerical data, a crucial step in language processing.
Lastly, we have “Transformer.” This is where BERT’s magic happens. The Transformer is an attention-based model that significantly boosts training speed. What makes the Transformer stand out is its parallelization capability, allowing BERT to train on vast amounts of data in a relatively short time. At its core, the Transformer employs an attention mechanism, enabling BERT to focus on the most important words in a sentence while disregarding the less relevant ones.
Transformer models can be broadly categorized into three types:
- Auto-regressive Transformer models or Encoder-only models
- Auto-encoding Transformer models or Decoder-only models
- Sequence-to-sequence Transformer models or Encoder-decoder models
BERT, specifically, falls into the Auto-regressive or encoder-only models category, as it primarily contains encoders.
Much like a knowledgeable human, BERT acquires knowledge. While humans gain knowledge through reading and learning, BERT’s knowledge comes from extensive training on vast textual sources, including Wikipedia (~2.5 billion words) and Google’s BooksCorpus (~800 million words). Once BERT has absorbed this knowledge, it becomes a versatile tool, capable of performing various tasks, such as answering questions, encoding text, summarizing text, selecting responses, retrieving similar content, translating languages, conducting sentiment analysis, and even comprehending medical or scientific texts.
BERT comes in two primary sizes: BERT Base and BERT Large. The Base version features twelve encoder layers, 768 feed-forward hidden layers, and 12 attention masks. In contrast, the Large version boasts twenty-four encoder layers, 1024 feed-forward hidden layers, and 16 attention masks.
BERT made its debut in 2018, and it ushered in a wave of different BERT models, each with its unique characteristics, including:
These models have expanded the possibilities of natural language understanding and processing, offering various options for different applications.