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Output Formula for a Polarity Classification Model
The output of a fine-tuned model, denoted as , for a new input sequence is a probability distribution over the predefined classes. For a polarity classification task, this output is a vector containing the conditional probabilities of the input being 'positive', 'negative', or 'neutral'. This is represented by the formula: .
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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
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Example of Text for Polarity Classification
Output Formula for a Polarity Classification Model
Example of a Prediction in Polarity Classification
A model, which has been specialized for a text classification task, processes a new input and produces the following probability distribution over three possible classes:
{"Bug Report": 0.15, "Feature Request": 0.75, "General Inquiry": 0.10}. Based on this output, what is the model's final prediction?A language model has been specialized to classify customer support tickets into categories like 'Billing Issue', 'Technical Support', or 'Account Question'. Arrange the following steps in the correct sequence to describe how this model would process a new, unseen customer ticket to make a prediction.
Applying a Specialized Language Model
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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.