What is the procedure to use a Sentence Transformer model in a zero-shot or few-shot learning scenario for a specific task?

To use a Sentence Transformer model in zero-shot or few-shot learning, you start by leveraging the model’s pre-trained ability to generate semantic embeddings for text. In zero-shot scenarios, you directly compare the embeddings of input text with embeddings of candidate labels or examples to make predictions without task-specific training. For few-shot learning, you incorporate a small number of labeled examples to guide the model’s understanding of the task, often by modifying how you structure comparisons between inputs and labels. The key steps involve encoding text into vectors, defining candidate labels or examples, and using similarity metrics like cosine similarity to infer relationships.

In zero-shot learning, begin by defining candidate labels representing possible outcomes for your task. For example, if classifying customer feedback into “complaint,” “praise,” or “inquiry,” encode each label (e.g., “This is a complaint”) into embeddings using the Sentence Transformer. Next, encode the input text (e.g., a user message) and compute cosine similarity between its embedding and each label’s embedding. The label with the highest similarity score is the predicted class. This works because the model’s pre-training on diverse text allows it to recognize semantic relationships between phrases, even without fine-tuning. For instance, the input “The product arrived damaged” would likely align most closely with the “complaint” label embedding. To improve results, craft labels that mimic natural language descriptions of categories rather than single words.

For few-shot learning, augment the process by including a small set of labeled examples (e.g., 3-5 per class) to provide context. One approach is to concatenate the examples with the input text before encoding. For example, prepend “Examples: Complaint: ‘My order is late.’ Praise: 'Great service!’” to the input text, then compare the combined embedding to candidate labels. Alternatively, encode each example separately and average their embeddings to create a “class prototype” for comparison. This helps the model recognize patterns specific to your task. For instance, if your examples include “Inquiry: 'When will this ship?’” the model learns to associate question-like structures with the “inquiry” label. The choice between concatenation and prototype averaging depends on computational constraints and whether context preservation is critical.

Practical considerations include selecting an appropriate Sentence Transformer model (e.g., “all-mpnet-base-v2” for accuracy, “all-MiniLM-L6-v2” for speed) and preprocessing text to match its training data format. Normalize embeddings before similarity calculations to ensure fair comparisons. For classification tasks, apply softmax to similarity scores to convert them into probabilities. If performance is subpar, experiment with label phrasing or add more examples in few-shot scenarios. While Sentence Transformers require no fine-tuning for basic zero/few-shot use, you can further fine-tune the model on your task data if labeled examples become available. Always validate results with a small test set to gauge effectiveness before deployment.