Unlocking the Secrets of Transformers: The Power of Self-Attention Scoring Efficiency (SASE)
In the realm of natural language processing (NLP), Transformers have revolutionized the way we approach language understanding and generation. At the heart of their success lies a crucial metric: Self-Attention Scoring Efficiency (SASE). This metric is a game-changer in evaluating the model's ability to focus on relevant input tokens while elegantly ignoring irrelevant ones.
What is SASE?
SASE measures the efficiency of self-attention mechanisms in Transformers. Self-attention allows the model to weigh the importance of each input token relative to the others, enabling it to focus on the most relevant information. By calculating the ratio of relevant attention scores to total attention scores, SASE provides a quantitative measure of the model's ability to selectively attend to key tokens.
Why is SASE important?
A higher SASE score (> 0.8) is a strong indicator of a model's a...
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