What is BERT?
BERT, Bidirectional Encoder Representations from Transformers, is a machine learning framework developed by Google in 2018 for natural language processing (NLP). It is designed to improve the contextual understanding of unlabeled text across various tasks by learning to predict the text that might come before and after (bi-directional) other text.
Why is BERT important?
BERT is important because it converts words into numbers, which is crucial for machine learning models that use numbers, not words, as inputs. This process allows you to train machine learning models on your textual data, transforming your text data to be used with other data types for making predictions in an ML model.
BERT's ability to understand the context of words makes it a powerful tool for various language tasks, such as:
- Determining the sentiment of movie reviews
- Helping chatbots answer questions
- Predicting text when writing emails
- Summarizing long legal contracts
- Differentiating words with multiple meanings based on the surrounding text
BERT pre-trained models deliver state-of-the-art results in NLP. Unlike directional models that read text sequentially, BERT models look at the surrounding words to understand the context. The models are pre-trained on massive volumes of text to learn relationships, giving them an edge over other techniques.
