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After the fine-tuning process, a complete BERT-based architecture for downstream tasks can be represented by the formula $$\mathrm{Predict}_{\tilde{\omega}}(\mathrm{BERT}_{\tilde{\theta}}(\cdot))$$. This denotes that the model is applied to new data using the optimized, fine-tuned parameters $$\tilde{\theta}$$ for the BERT encoder and $$\tilde{\omega}$$ for the prediction network. The specific form of the downstream task dictates both the input and output formats of this model, as well as the underlying architecture of the prediction network layered on top of the BERT encoder.

Parameterized Prediction Function using a BERT model

Given the system described in the case study, which component corresponds to the $Predict_{\tilde{\omega}}(\cdot)$ part of the general formula $Predict_{\tilde{\omega}}(BERT_{\tilde{\theta}}(\cdot))$? Explain your reasoning.

Sentiment Analysis System Design

A machine learning model designed to identify the sentiment of customer reviews is represented by the function $SentimentClassifier_{\tilde{\omega}}(TextEncoder_{\tilde{\theta}}(review\_text))$. Analyze this structure by explaining the specific role of the $TextEncoder_{\tilde{\theta}}$ component versus the $SentimentClassifier_{\tilde{\omega}}$ component. In your explanation, also describe what the parameters $\tilde{\theta}$ and $\tilde{\omega}$ likely control for each respective component.

Component Roles in a Two-Stage Prediction Model

A machine learning team is developing a model to classify medical research abstracts into different fields of study. They use a large, pre-trained language model as a foundation. Their complete system is represented by the function $Classifier_{\tilde{\omega}}(Embedder_{\tilde{\theta}}(text))$, where $Embedder_{\tilde{\theta}}$ is the pre-trained model and $Classifier_{\tilde{\omega}}$ is a new component. During training on their specific dataset of abstracts, the team chooses to only update the

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