How can Sentence Transformer embeddings be used for downstream tasks like text classification or regression?

Sentence Transformer embeddings are used for downstream tasks like text classification or regression by converting text into fixed-dimensional vectors that capture semantic meaning. These vectors serve as input features for traditional machine learning models or neural networks. For classification, embeddings train models to predict labels (e.g., sentiment categories). For regression, they predict numerical values (e.g., relevance scores). The embeddings abstract text into dense representations, allowing models to focus on learning task-specific patterns without handling raw text directly.

For text classification, a common approach is to generate embeddings for each text sample and pair them with labels. For example, in sentiment analysis, you might use sentence-transformers/all-MiniLM-L6-v2 to convert customer reviews into 384-dimensional vectors. These vectors are fed into a classifier like logistic regression, a support vector machine (SVM), or a simple neural network in PyTorch. Similarly, for regression tasks—like predicting a readability score between 0 and 1—you could train a linear regression model or a shallow neural network on the embeddings. The key advantage is that the embeddings encode semantic relationships, so phrases like “excellent service” and “great experience” cluster closely, helping models generalize better.

Implementation involves two steps. First, use the Sentence Transformers library to generate embeddings for your dataset. For example, model.encode(texts) converts a list of texts into a NumPy array of embeddings. Second, split the data into training and test sets, then train a downstream model. If performance is lacking, you can fine-tune the Sentence Transformer on your task-specific data using techniques like contrastive loss to better align embeddings with your task. For lightweight deployments, pre-trained embeddings paired with simple models (e.g., scikit-learn’s SGDClassifier) work efficiently. If computational resources allow, adding layers like a feedforward network on top of embeddings can capture non-linear patterns. Preprocessing like normalization (scaling embeddings to unit vectors) often improves stability for downstream models.