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The overall goal of this post is to demystify summarization evaluation to help teams better benchmark performance on this critical capability as they seek to maximize value. Use it as a baseline or benchmark for summary quality related to content selection. ROUGE would not identify these issues. and expects a response from the model.
We also share the key technical challenges that were solved during construction of the Face-off Probability model. At the end, we found that the LightGBM model worked best with well-calibrated accuracy metrics. How it works. Imagine the following scenario: It’s a tie game between two NHL teams that will determine who moves forward.
Regular reviews ensure that quality benchmarks are being met and provide valuable feedback for continuous improvement in customer interactions. Regular calibration sessions with QA evaluators help ensure consistency and alignment across the team. Solution: Balance constructive criticism with positive reinforcement.
Each trained model needs to be benchmarked against many tasks not only to assess its performances but also to compare it with other existing models, to identify areas that needs improvements and finally, to keep track of advancements in the field. Evaluating these models allows continuous model improvement, calibration and debugging.
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