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Sentence Similarity Calculation using BERT-based Regression
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.
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Ch.2 Generative Models - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Ch.1 Pre-training - Foundations of Large Language Models
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Sentence Similarity Calculation using BERT-based Regression
Illustration of BERT for Text-Pair Tasks (Classification and Regression)
Training BERT-based Regression Models via Loss Minimization
Adapting a Language Model for a New Task
A data science team has a pre-trained transformer model that has been successfully fine-tuned for a text classification task, predicting whether a product review is 'positive' or 'negative'. They now want to adapt this model for a new regression task: predicting a continuous 'star rating' for reviews, on a scale from 1.0 to 5.0. Which of the following modifications represents the most direct and essential change to the model's architecture to enable this new task?
Comparing Model Architectures for Different NLP Tasks
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Model Adaptation for Similarity Scoring
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?
Designing a Semantic Search Component