Transformers Explained | Understand the model behind GPT, BERT, and T5
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[Transformers Explained](https://www.youtube.com/watch?v=SZorAJ4I-sA) - Transformers are a type of [neural network](/posts/What-Is-a-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. see [Introduction to Large Language Models](/posts/Introduction-to-Large-Language-Models) for more.