Evaluating Model Output for Polarity Classification
Based on the fundamental properties of a probability distribution for a classification task, which sentence's output is problematic, and why? Explain the mathematical property that is being violated.
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Ch.1 Pre-training - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Analysis in Bloom's Taxonomy
Cognitive Psychology
Psychology
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A language model is fine-tuned for a sentiment analysis task with three possible labels: 'positive', 'negative', and 'neutral'. When given a new sentence, the model produces the following output vector, where each value corresponds to the probability of the respective label in the given order:
[0.15, 0.80, 0.05]. How should this output be interpreted to determine the final classification?Evaluating Model Output for Polarity Classification
A language model fine-tuned for a three-class polarity classification task (positive, negative, neutral) produces an output vector for a new input. The sum of the values in this output vector must equal 1.0.