Bidirectional Encoder Representations from Transformers (BERT)

BERT is a language model that has taken the field of natural language processing (NLP) by storm. Its ability to automate language understanding has made it a powerful tool for various language tasks, including sentiment analysis, question answering, text prediction, summarization, and more.

It has been used by Google to surface more relevant search results, chatbots to provide more accurate responses, and voice assistants like Alexa and Siri to better understand and respond to user requests. Its success has been made possible by leveraging a massive dataset of 3.3 billion words, including Wikipedia and Google’s BooksCorpus.

One of the ways BERT works is through a technique called Masked Language Modeling (MLM), where a random 15% of tokenized words are hidden during training, and BERT’s job is to correctly predict the hidden words by using bidirectional context clues. BERT also uses Next Sentence Prediction (NSP), which helps it learn about the relationships between sentences by predicting if a given sentence follows the previous sentence or not.

Transformers, a deep-learning algorithm that uses attention to observe relationships between words, has made it possible to train BERT on large amounts of data in a relatively short period. BERT has successfully achieved state-of-the-art accuracy on 11 common NLP tasks, outperforming previous top NLP models, and is the first to outperform humans.

However, the training of large machine learning models like BERT has a significant environmental impact, which is why open-source libraries are essential in reducing the overall compute cost of the community-driven efforts. BERT’s source code is publicly accessible, and developers can get started using BERT by fine-tuning the model to customize its performance to their unique tasks.

BERT is a highly advanced and complex language model that has revolutionized the field of NLP. Its state-of-the-art performance is supported by training on massive amounts of data and leveraging Transformers architecture. Thanks to the open-source library and the incredible AI community’s efforts to continue to improve and share new BERT models, the future of untouched NLP milestones looks bright. The question is, what will you create with BERT?

Here are some instructions on how to set it up!