Zero-shot text classification

Classify a text into a set of labels you supply — intent, topic, category, routing queue, sentiment buckets, anything — without training data. Returns label (the best match, always one of your labels), labels (single-label: just the best match; multi-label: every label whose score >= threshold, possibly empty), scores (one {label, score} per input label, descending; a distribution in single-label mode, independent 0-1 applicability in multi-label mode) and a one-sentence rationale. Labels can be plain names or {name, description} objects — a short description per label sharpens borderline decisions. Use context to say what the texts are (e.g. "support tickets for a billing product"). 2-20 labels.