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Role of the Final Classification Component
A machine learning model is being developed to categorize customer reviews into 'Happy', 'Unhappy', or 'Neutral'. The first part of the model processes a review and outputs a 512-dimensional numerical vector that represents the review's meaning. Describe the specific role and expected output of the final component that is added on top of this first part to complete the classification task.
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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
Application in Bloom's Taxonomy
Cognitive Psychology
Psychology
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Empirical Science
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A text classification model is designed to categorize sentences into one of three classes: 'Positive', 'Negative', or 'Neutral'. The model works in two stages: first, it generates a unique numerical vector representation for each input sentence. Second, a final component takes this vector and outputs a probability distribution over the three classes. During testing, you observe that for a wide variety of different input sentences, the model consistently outputs probabilities that are very close to uniform (e.g., {'Positive': 0.33, 'Negative': 0.34, 'Neutral': 0.33}). Based on this specific symptom, what is the most direct and likely cause of the problem?
Role of the Final Classification Component
A component is added to a model to predict a probability distribution over a set of predefined classes based on an input feature representation. Match each element of this component to its specific function.