BERT (Bidirectional Encoder Representations from Transformers) is a pre-trained Transformer-based model that excels in understanding word context. BERT handles word context by utilizing attention mechanisms and training on large-scale corpora.

  To understand the context of a word, BERT employs a method called "masked language modeling" during pre-training. This involves randomly masking out some of the words in a sentence and then training the model to predict the masked words based on the surrounding context. By learning to predict the masked words bidirectionally, BERT takes into account both the left and right context of each word.

  BERT also utilizes the attention mechanism to capture the relationships between words in a sentence. Attention allows the model to assign different weights to different words based on their importance in understanding the context. As a result, BERT can focus more on the relevant words and ignore the irrelevant ones, thereby capturing the fine-grained contextual information.

  During pre-training, BERT is trained on a massive amount of text data, such as Wikipedia and books, to learn general language representations. This enables the model to capture a wide range of contextual information across different types of text. By pre-training on such diverse datasets, BERT is equipped with the ability to handle a variety of word context situations.

  In summary, BERT handles word context by using masked language modeling and attention mechanisms. By pre-training on large-scale corpora, BERT learns to understand and represent the contextual relationships between words, making it proficient at capturing fine-grained word context.