self-attention mechanism
using self-attention
self-attention layer
apply self-attention
self-attention weights
with self-attention
self-attention scores
self-attention model
self-attention network
self-attention improved
the model utilizes self-attention to weigh the importance of different words in the input sequence.
self-attention allows the transformer to capture long-range dependencies effectively.
we fine-tuned the pre-trained model with self-attention on a new dataset.
the self-attention mechanism significantly improved the model's performance on the task.
visualizing self-attention weights provides insights into the model's reasoning process.
multi-head self-attention enables the model to attend to different aspects of the input.
self-attention layers are crucial for understanding context in natural language processing.
the self-attention mechanism helps the model resolve ambiguity in the sentence.
we compared self-attention with traditional recurrent neural networks.
the effectiveness of self-attention is well-established in the field of nlp.
self-attention contributes to better machine translation quality.
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