A machine learning architecture used primarily in the field of natural language processing (NLP).
More about Transformer Models:
Transformer models are a type of neural network architecture that has revolutionized the field of natural language processing. They are designed to process sequential data, particularly language, for tasks such as translation, summarization, and text generation, utilizing mechanisms like self-attention to understand the context and relationships in text.
Frequently Asked Questions
How do transformer models differ from earlier neural networks?
Transformer models use self-attention mechanisms to weigh the significance of different parts of the input data, which is a departure from earlier sequence-based models that processed data in order.
What makes transformer models effective for language tasks?
Their ability to process words in relation to all other words in a sentence simultaneously allows for more nuanced understanding and generation of language.
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