Outline the primary architectural differences between BERT and the original Transformer model.

The primary architectural difference between BERT (Bidirectional Encoder Representations from Transformers) and the original Transformer model lies in its use of only the encoder stack and its pre-training objective. The original Transformer model consists of both an encoder and a decoder stack, designed for sequence-to-sequence tasks like machine translation. BERT, however, only utilizes the encoder stack of the Transformer. This is because BERT is primarily designed for understanding the input sequence, rather than generating a new sequence. The second key difference is BERT's pre-training objective. The original Transformer was typically trained for specific translation tasks. BERT is pre-trained on a large corpus of text using two unsupervised tasks: Masked Language Modeling (MLM) and Next Sentence Prediction (NSP). MLM involves randomly masking some of the words in the input sequence and training the model to predict the masked words based on the context provided by the unmasked words. NSP involves training the model to predict whether two given sentences are consecutive sentences in the original text. This pre-training allows BERT to learn general-purpose language representations that can then be fine-tuned for a variety of downstream NLP tasks, such as text classification, question answering, and named entity recognition. Because BERT only uses the encoder stack, it processes the entire input sequence at once, allowing it to learn bidirectional contextual representations. In contrast, the decoder in the original Transformer processes the input sequence autoregressively, generating the output sequence one token at a time. Therefore, BERT focuses on learning rich representations of the input, while the original Transformer is designed for both encoding and decoding sequences.