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Transformers are a powerful class of deep learning models that have become increasingly popular in recent years, particularly for natural language processing (NLP) tasks. Here are a few common applications of transformers:

Language Translation: Transformers are particularly effective at machine translation, i.e., translating text from one language to another. They have been used in popular translation tools like Google Translate and Microsoft Translator.
Language Modeling: Transformers are also used for language modeling, which is the task of predicting the next word in a sequence of text. This is useful for various applications like speech recognition and predictive text input.
Sentiment Analysis: Sentiment analysis is the process of identifying the emotional tone of a piece of text. Transformers have been used to build sentiment analysis models that can accurately identify the sentiment of a text, which can be useful for various applications like social media monitoring and customer service.
Question Answering: Transformers have been used to build models that can answer questions posed in natural language. This can be used for various applications like customer support, where a chatbot can answer customer queries.
Image Captioning: Transformers have also been used for image captioning, where the model generates a natural language description of an image. This can be useful for various applications like automatic image tagging and accessibility for visually impaired individuals.
These are just a few examples of the many applications of transformers. As the field of deep learning continues to evolve, we can expect to see even more innovative applications of this technology.