mlpeek: classify text into your own labels (zero-shot)
classify_zero_shotClassify English text into 1-10 custom labels without training data; use single-label or multi-label scoring to tag content by your chosen categories.
Instructions
mlpeek: Classify a text into 1-10 of your labels. For named entities use extract_entities. Returns scores of 1-10 caller-supplied labels for an English text with the nli-deberta-v3-xsmall NLI cross-encoder (Apache-2.0): single-label scores sum to 1 (softmax over the labels), multi_label scores each label on its own; sorted best first. Scores are model confidences, not calibrated probabilities. English only. Runs an open model pinned by hash on Tanod's own CPU (no LLM, no third-party API); an unavailable model is a 503 (not charged). Typically 0.1-0.2 s for a short text and 5 labels, up to about 5 s at 10 labels. Price: USD 0.001. Free: 5 mlpeek calls per IP per UTC day. The submitted text is never logged or stored. Docs: https://tanod.dev/learn/named-entity-recognition-zero-shot-classification-api.html
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | English text, at most 2,000 characters. | |
| labels | Yes | 1-10 unique candidate labels, each at most 100 characters. | |
| multi_label | No | Score each label on its own instead of making them compete. | |
| hypothesis_template | No | Must contain {} exactly once. | This example is about {}. |
Output Schema
| Name | Required | Description | Default |
|---|---|---|---|
| note | No | ||
| error | No | Only on an error result: an object {code, message}, or the reason string of an x402 PaymentRequired object. | |
| model | No | ||
| usage | No | ||
| labels | No | ||
| truncated | No | ||
| multi_label | No | ||
| hypothesis_template | No |