self-attention

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.