To use a Sentence Transformer model in zero-shot or few-shot learning, you first leverage its ability to generate semantic embeddings for text, which can be compared to determine similarity. In zero-shot scenarios, you define task-specific labels or categories as textual descriptions, encode them into embeddings, and compare them to embeddings of input data to make predictions. For few-shot learning, you use a small set of labeled examples to fine-tune a classifier on top of the embeddings or adjust the similarity scoring process.
In a zero-shot setup, start by selecting a pre-trained Sentence Transformer model (e.g., all-mpnet-base-v2) that generalizes well across domains. Define your task’s possible classes as natural language descriptions (e.g., "This text is about politics" for a news categorization task). Encode both the input text and these label descriptions into embeddings using the model. Compute similarity scores (e.g., cosine similarity) between the input embedding and each label embedding. The label with the highest similarity becomes the predicted class. For example, in sentiment analysis, you might compare an input sentence to embeddings of "This review is positive" and "This review is negative" to infer sentiment without any task-specific training.
For few-shot learning, augment the approach by incorporating a small number of labeled examples (e.g., 5–20 per class). Encode these examples and their labels, then use them to train a lightweight classifier (e.g., logistic regression, k-nearest neighbors) on the embeddings. Alternatively, you can structure the input text with examples in a prompt-like format (e.g., "Movie: X, Sentiment: Positive. Movie: Y, Sentiment: Negative. Movie: Z, Sentiment: ___") before encoding, leveraging the model’s ability to infer patterns from context. This approach is useful when classes are nuanced and require contextual understanding, such as detecting sarcasm or domain-specific intents. The key advantage of Sentence Transformers here is that their high-quality embeddings reduce the need for large datasets, making them practical for low-data scenarios.