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They introduced the attention mechanism that allows models to weigh the importance of different words.
They focused solely on recurrent neural networks for better sequence processing.
They eliminated the need for any neural networks by using traditional algorithms.
They restricted the model's architecture to a fixed sequence length for simplicity.
Understanding the Answer
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The developers created a new way for models to focus on important words by using attention. Other options are incorrect because Some think the team only used recurrent neural networks (RNNs); It is wrong to say they stopped using neural networks.
Key Concepts
Contributors to Transformer Model
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Deep Dive: Contributors to Transformer Model
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Definition
Several individuals have made significant contributions to the development of the Transformer model. Each contributor played a unique role in designing, implementing, and improving different aspects of the model, leading to its success in machine translation tasks.
Topic Definition
Several individuals have made significant contributions to the development of the Transformer model. Each contributor played a unique role in designing, implementing, and improving different aspects of the model, leading to its success in machine translation tasks.
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