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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 Transformer creators used a new idea called attention, which lets the model look at all words in a sentence and choose which ones matter most. Other options are incorrect because Many think the team only worked on recurrent neural networks, but they actually abandoned those in favor of attention; Some believe the Transformers stopped using neural networks entirely, but they are built from deep learning layers.
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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