Component Roles in a Probabilistic Text Classifier
A text classification model is represented by the formula . In your own words, explain the distinct roles of the 'Encode' and 'Classify' components in the process of determining the final probability for a given input text .
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Ch.1 Pre-training - Foundations of Large Language Models
Foundations of Large Language Models
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A text classification model is designed with two sequential components: an 'encoder' that transforms an input sentence into a numerical vector, and a 'classifier' that uses this vector to predict a category. During evaluation, it is discovered that the model performs poorly. A detailed inspection reveals that semantically opposite sentences, such as 'The movie was brilliant and captivating' and 'The movie was dull and boring', are both being transformed into nearly identical numerical vectors by the encoder. Based on this specific observation, what is the most accurate analysis of the problem?
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Component Roles in a Probabilistic Text Classifier