# Natural Language Processing with Transformers Transformers are a class of models introduced in the seminal paper "Attention is All You Need" by Vaswani et al. (2017). They leverage a mechanism called 'attention' to weigh the significance of different words in the input data when generating an output prediction. ```python from transformers import BertTokenizer, BertModel import torch tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') model = BertModel.from_pretrained('bert-base-uncased') ``` Here, we're using the BERT (Bidirectional Encoder Representations from Transformers) model, a popular transformer model pretrained on a large corpus of text. Transformers process input data in parallel, unlike RNNs (Recurrent Neural Networks) and they are not sensitive to the order of the data, which is why Positional Encoding is used. Positional Encoding adds information about the position of the words in the sentence, which is crucial for tasks like question answering. ```python inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") outputs = model(**inputs) ``` In this code snippet, we're tokenizing a sentence (converting words to their corresponding IDs in the model vocabulary), and then feeding it to the model. The model then returns the hidden states from the last layer of the model. Transformer models have been used for a variety of NLP tasks, such as sentiment analysis, text generation, and translation. Their ability to handle long-range dependencies and their parallelizable nature makes them a powerful tool for NLP.