Transformers are a type of neural network architecture that has revolutionized natural language processing.
They can translate text, generate poems, write op-eds, and even generate computer code.
Transformers are based on the concepts of positional encodings and attention mechanisms, specifically self-attention.
Positional encodings capture word order by assigning a number to each word in a sentence, enabling the network to understand the importance of word order.
Attention mechanisms allow the model to look at every word in the input sentence when making decisions about the output sentence, capturing language nuances like gender and word order.
Self-attention helps the model understand language in context, disambiguating word meanings, identifying parts of speech, and recognizing tense.
Transformers, such as BERT, have become widely used in natural language processing tasks like text summarization, question answering, and classification.
They can be trained on large text corpora, including unlabeled data, using semi-supervised learning techniques.
Pretrained transformer models are available for download from TensorFlow Hub and the Hugging Face library, making it easier to incorporate transformers into applications.