Yes, BERT (Bidirectional Encoder Representations from Transformers) does consider word order in its representations. BERT is a state-of-the-art pre-training language model that is designed to capture the contextual understanding of words within a sentence by considering the order in which they appear.

  BERT uses a transformer-based architecture, which allows it to effectively model the relationships between words in a sentence. The transformer model consists of self-attention mechanisms that weigh the importance of different words based on their positions relative to each other. This attention mechanism enables BERT to capture dependencies and contextual relationships between words that are crucial for understanding their meanings.

  During the pre-training phase, BERT is trained on a large corpus of text, which allows it to learn the statistical patterns and co-occurrence information of words. By employing a bidirectional training objective, BERT is able to leverage both the preceding and following words to better understand the current word in context. This bidirectional modeling approach helps BERT capture long-range dependencies and deeply encode the word order information.

  In summary, BERT considers word order in its representations by utilizing transformer-based architecture and bidirectional training objective. This enables it to effectively capture the dependencies and contextual relationships between words, leading to its strong performance in various natural language processing tasks.