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A practical application of BERT-based regression is to calculate the similarity between two sentences. This is achieved by adapting the prediction network, for example by adding a Sigmoid layer, to output a real-valued similarity score.

Sentence Similarity Calculation using BERT-based Regression

A team is adapting a pre-trained transformer model, originally built for text classification, to calculate a similarity score between pairs of sentences. The goal is to have the model output a single value between 0 (completely different) and 1 (identical). After training, they observe that the model's output is a single, unbounded number (e.g., -12.7, 0.5, 23.9), which is not the desired format. What specific component is most likely missing from the final layer of their model architecture, and why is this component necessary to achieve the desired output?

Model Adaptation for Similarity Scoring

Based on the provided case study, what specific modification should be made to the model's final output layer, and why is this change essential for the system to function correctly?

Designing a Semantic Search Component

A developer is building a system to identify duplicate questions on a Q&A website. The system needs to take two questions as input and output a single similarity score ranging from 0.0 (completely different) to 1.0 (identical). The developer plans to use a pre-trained transformer-based model. Which modification to the model's final output layer is the most suitable for this regression task?

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