Sentence Transformer embeddings can be used for downstream tasks like text classification or regression by serving as dense numerical representations of text that capture semantic meaning. These embeddings are generated by passing input text through a pre-trained Sentence Transformer model, which outputs fixed-length vectors. These vectors encode the contextual and semantic information of the text, making them useful as input features for traditional machine learning models or neural networks.
For text classification, the embeddings act as feature vectors that replace raw text. For example, you could train a logistic regression classifier or a support vector machine (SVM) using these embeddings as input. The model learns to map the embeddings to predefined classes (e.g., sentiment labels like "positive" or "negative"). Alternatively, you can build a neural network with a classification head (e.g., a feed-forward layer followed by softmax) on top of the embeddings. If labeled data is limited, you can freeze the Sentence Transformer weights and train only the classification layers. For domain-specific tasks (e.g., medical text classification), fine-tuning the Sentence Transformer alongside the classifier using task-specific data often improves performance by aligning the embeddings with the target domain.
For regression tasks (e.g., predicting a numerical score for text readability), the process is similar. The embeddings are used as input to regression models like linear regression, decision trees, or neural networks with a single output neuron. For instance, in a customer feedback analysis system, embeddings of user reviews could be fed into a regression model to predict a satisfaction score between 1 and 10. The key advantage is that the embeddings abstract away text complexity, allowing the regression model to focus on learning relationships between semantic features and the target variable. Fine-tuning the Sentence Transformer on regression-specific data (e.g., using a mean squared error loss) can further refine embeddings for the task.
In both cases, Sentence Transformers reduce the need for manual feature engineering. Pre-trained embeddings work well out-of-the-box for general tasks, while fine-tuning adapts them to specialized domains. The embeddings can be integrated into traditional scikit-learn pipelines or modern deep learning frameworks (PyTorch/TensorFlow), offering flexibility in implementation. For example, a developer might use sentence-transformers/all-MiniLM-L6-v2 to generate embeddings, then apply a lightweight XGBoost classifier for efficient deployment.