To implement zero-shot classification with embeddings, you’ll use pre-trained language models to convert text and labels into numerical vectors (embeddings), then compare their similarity. This approach works without task-specific training because the embeddings capture semantic meaning, allowing you to classify unseen categories by measuring how closely input text aligns with candidate labels. Here’s a step-by-step breakdown.
First, generate embeddings for your input text and candidate labels using a pre-trained model. For example, you could use the sentence-transformers library in Python, which provides models like all-MiniLM-L6-v2 optimized for semantic similarity. For a support ticket like “My payment failed twice today,” compute its embedding. Then, create embeddings for potential labels such as “Billing Issue,” “Technical Error,” and “Account Access.” The model converts each text string into a high-dimensional vector representing its meaning. This step relies on the model’s ability to generalize semantic relationships learned during pre-training.
Next, calculate similarity scores between the input embedding and each label embedding. Cosine similarity is commonly used here because it measures the angle between vectors, which correlates with semantic closeness. For example, if the input embedding’s cosine similarity is highest with “Billing Issue,” that label becomes the predicted class. Libraries like NumPy or scikit-learn provide tools for efficient similarity computation. You can also rank labels by score to show confidence levels or handle multi-label scenarios. This method works best when label descriptions are clear and distinct, as overlapping or vague labels may produce ambiguous similarity scores.
Practical considerations include choosing the right model and optimizing label phrasing. Smaller models like all-MiniLM-L6-v2 are fast and suitable for real-time applications, while larger models (e.g., bert-base-nli-mean-tokens) may offer better accuracy at the cost of speed. Label wording matters: “Billing Problem” might align better with user queries than “Payment.” Experiment with synonyms or rephrasing labels to improve results. Additionally, preprocess input text to remove noise (like typos or irrelevant details) and ensure consistency. If performance is inconsistent, consider fine-tuning the embedding model on domain-specific data, though this adds complexity. Tools like Hugging Face’s transformers or OpenAI’s API (for paid solutions) offer alternatives if you need more customization or scalability.