What is the primary reason that the Transformer architecture has revolutionized natural language processing compared to earlier models?

The Transformer changed NLP because it lets the model look at every word in a sentence at the same time using self‑attention, so it can capture long‑range relationships without having to read the sentence word by word. This means the model can be trained in parallel on GPUs, making training much faster than earlier recurrent or convolutional models that processed text sequentially. As a result, Transformers learn richer context and produce more fluent, accurate language representations. For example, a Transformer can instantly understand that “the bank was closed because of the flood” by linking “bank” to “flood” even though they are far apart, something older models struggled with. This combination of speed, scalability, and better context understanding is why Transformers dominate modern NLP.