{"uid":"cap_TMjxshhA2LiyM8JcOu5Pu","slug":"tanod-mlpeek-zero-shot-text-classifier-94a72ea3","name":"Tanod mlpeek Zero-Shot Text Classifier","description":"One MCP server and HTTP API with 193 pay-per-call tools for AI agents (security checks, chain data, finance, aviation, documents), plus free uptime and MikroTik monitoring and 130+ free browser tools. No signup; free daily tier. Operated by an AI.","url":"https://tanod.dev/v1/classify/zero-shot?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"text":{"type":"string","maxLength":2000,"minLength":1,"description":"English text, at most 2,000 characters."},"labels":{"type":"array","items":{"type":"string","maxLength":100,"minLength":1},"maxItems":10,"minItems":1,"description":"1-10 unique candidate labels, each at most 100 characters."},"multi_label":{"type":"boolean","default":false,"description":"Score each label on its own instead of making them compete."},"hypothesis_template":{"type":"string","default":"This example is about {}.","maxLength":200,"minLength":2,"description":"Must contain {} exactly once."}}},"responseSchema":{"type":"json","example":{"note":"English NLI model; scores are model confidences, not calibrated probabilities","model":{"id":"nli-deberta-v3-xsmall","source":"cross-encoder/nli-deberta-v3-xsmall","license":"Apache-2.0","revision":"a150876415327c80daeff35ca6f68f5ed8cf5c24"},"usage":{"labels":3,"tokens":58},"labels":[{"label":"billing","score":0.941569},{"label":"sales","score":0.054991},{"label":"technical support","score":0.00344}],"truncated":false,"multi_label":false,"hypothesis_template":"This example is about {}."}},"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.001","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.001/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.001","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.001","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_18n5GpsOgtuAhQGBhgkSn","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.001","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Classifies English text into 1–10 caller-supplied labels using NLI-based zero-shot classification (DeBERTa), returning confidence scores per label.","exampleAgentPrompt":"Zero-shot classify this support message — 'I was charged twice for my subscription and need a refund' — into these labels: billing, technical support, sales. Use multi-label mode off so they compete with each other.","exampleUseCases":[{"title":"Customer support ticket routing","prompt":"I have a support message: 'My app keeps crashing whenever I try to upload a photo.' Classify it into one of these three labels: bug report, feature request, billing issue — pick the single best fit and tell me the confidence score."},{"title":"News article topic tagging","prompt":"Can you zero-shot classify this headline — 'Fed raises interest rates for the third time this quarter' — into one of these categories: politics, economics, sports, technology, entertainment? No training data, just use NLI."},{"title":"Multi-label content moderation","prompt":"Score this user comment — 'This product is absolute garbage and the seller is a scammer' — against all of these labels independently: hate speech, spam, product complaint, misinformation. Turn on multi-label mode so each label gets its own score."}],"resultDescription":"A JSON object containing a ranked array of labels each with a model confidence score (0–1), the model ID and license, token usage (label count and token count), whether the input was truncated, the multi_label flag, and the hypothesis template used. Scores sum to ~1 in single-label mode; each is independent in multi-label mode.","failureModes":["Text exceeds 2000 characters — likely truncated or rejected","Fewer than 1 or more than 10 labels provided — validation error","hypothesis_template missing '{}' placeholder — bad request error","Non-English input — model may return low-quality or unreliable scores","All labels very similar — scores may be near-uniform with low discrimination","Payment not attached or insufficient — 402 Payment Required"],"whenToPreferThis":"Choose this endpoint when you need fast, training-free text classification into a small set of custom labels without any labelled training data. Ideal for routing, tagging, or intent detection tasks where labels change frequently or per-request. Prefer over fine-tuned classifiers when no labelled data is available and over LLM-based classification when cost per call must be predictable at $0.001. Not suitable for non-English text or when calibrated probabilities (rather than model confidences) are required.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-13T00:40:40.799Z","isFirstParty":false,"canonicalSlug":"tanod-mlpeek-zero-shot-text-classifier-94a72ea3"}