What type of model like BERT considers the context of words in both directions within a sentence?

The bidirectional model is designed to take into account the context of words from both directions within a sentence, meaning it analyzes the text by considering the words that come before and after a given word. This approach allows the model to understand the full context and nuances of language, which is essential for many natural language processing tasks.

In contrast to unidirectional models, which only consider preceding words to predict subsequent words, bidirectional models employ mechanisms like attention and transformers to retain a broader context. This is particularly beneficial for tasks such as sentiment analysis, named entity recognition, or any other applications that require a deep understanding of language.

Recurrent neural networks are often used in natural language processing and have the ability to retain information in sequences, but they are not inherently bidirectional unless specifically designed that way. Convolutional neural networks, typically used for image processing, do not traditionally handle sequential data or context in the manner necessary for understanding language.

Thus, the distinction of considering context in both directions is what makes the bidirectional model, such as BERT, particularly powerful in language understanding tasks.