rily in the field of NLP.

Like RNNs, Transformers are designed to handle sequential data, such as natural
language, for tasks such as translation and text summarization. However, unlike
RNNs, Transformers do not require that the sequential data be processed in
order. For example, if the input data is a natural language sentence, the
Transformer does not need to process the beginning of it before the end. Due to
this feature, the Transformer allows for much more parallelization than RNNs
and therefore reduced training times.

Since their introduction, Transformers have become the model of choice for
tackling many problems in NLP, replacing older recurrent neural network models
such as the long short-term memory LSTM. Since the Transformer model
facilitates more parallelization during training, it has enabled training on
larger datasets than was possible before it was introduced. This has led to the
development of pretrained systems such as BERT and GPT, which have been trained
with huge general language datasets, and can be fine-tuned to specific language
tasks.

Why?
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