{"uid":"cap_4lF90E8mG8P52y6bkfGY0","slug":"meeting-topic-local-text-classifier-naive-bayes-09b6a4ff","name":"Meeting Topic Local Text Classifier (Naive-Bayes)","description":"Meeting topic - Train local text baseline and classify: Train local text baseline and classify for Meeting topic. Up to 200 supplied labelled text records. Local dataset tools, evaluation and small naive-Bayes baselines; no hosted model or foundation-model training. One of 25 profiles or calculations across 20 shared operations in this sector. 0.01 USDC includes one bounded train local text baseline and classify deliverable.","url":"https://market.datapackvibe.com/x402/assistant-sector-ml-meeting-topic-local-classifier?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"records":{"type":"array","items":{"type":"object","required":["id","text","actual"],"properties":{"id":{"type":"string","default":"","maxLength":80,"minLength":1},"text":{"type":"string","default":"","maxLength":1000},"actual":{"enum":["planning","progress","decision","review"],"type":"string"}},"additionalProperties":false},"default":[{"id":"example-0-0","text":"planning sample document number 0 for this supplied dataset.","actual":"planning"},{"id":"example-0-1","text":"planning sample document number 1 for this supplied dataset.","actual":"planning"},{"id":"example-0-2","text":"planning sample document number 2 for this supplied dataset.","actual":"planning"},{"id":"example-1-0","text":"progress sample document number 0 for this supplied dataset.","actual":"progress"},{"id":"example-1-1","text":"progress sample document number 1 for this supplied dataset.","actual":"progress"},{"id":"example-1-2","text":"progress sample document number 2 for this supplied dataset.","actual":"progress"},{"id":"example-2-0","text":"decision sample document number 0 for this supplied dataset.","actual":"decision"},{"id":"example-2-1","text":"decision sample document number 1 for this supplied dataset.","actual":"decision"},{"id":"example-2-2","text":"decision sample document number 2 for this supplied dataset.","actual":"decision"},{"id":"example-3-0","text":"review sample document number 0 for this supplied dataset.","actual":"review"},{"id":"example-3-1","text":"review sample document number 1 for this supplied dataset.","actual":"review"},{"id":"example-3-2","text":"review sample document number 2 for this supplied dataset.","actual":"review"}],"maxItems":200,"minItems":2},"query_texts":{"type":"array","items":{"type":"string","default":"","maxLength":1000},"default":["planning sample"],"maxItems":20,"minItems":1}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.01","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":"$0.01/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_a57t7NoWf5D2LsypeI4jV","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.01","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Trains a local naive-Bayes text classifier on up to 200 labelled meeting-transcript records and classifies new query texts into planning, progress, decision, or review categories.","exampleAgentPrompt":"I have about 50 labelled meeting text snippets — some marked planning, some progress, some decision, and some review — can you train a local naive-Bayes classifier on them and then tell me what category these three new meeting notes fall into?","exampleUseCases":[{"title":"Auto-tag meeting agenda items","prompt":"I have 80 labelled examples of meeting agenda text already tagged as planning, progress, decision, or review — train a classifier on them and then classify these 5 new agenda snippets so I know how to route them."},{"title":"Evaluate meeting transcript classifier","prompt":"I want to see how well a simple naive-Bayes model does on our internal meeting notes — here are 120 labelled records across planning, progress, decision, and review; train the baseline and classify these 10 holdout texts."},{"title":"Categorize customer success meeting notes","prompt":"We have weekly customer success meetings and I want to automatically sort the notes into planning, progress, decision, and review buckets — I've got 60 labelled examples ready, can you train a local classifier and predict categories for these new entries?"}],"resultDescription":"Returns per-query predicted meeting topic labels (planning, progress, decision, or review) derived from a locally-trained naive-Bayes text baseline fitted on the supplied labelled records, along with evaluation metadata about the trained model.","failureModes":["Fewer than 2 records supplied (minItems violation)","More than 200 records supplied (maxItems exceeded)","Missing required fields (id, text, actual) on any record","Invalid actual label value outside enum (planning/progress/decision/review)","Text exceeds 1000-character limit per record","No query_texts provided or query_texts exceeds 20 items","Insufficient class diversity for a meaningful classifier (e.g. only one class represented)"],"whenToPreferThis":"Choose this endpoint when you need a fast, cheap, interpretable text classification baseline for meeting topics using only your own labelled data — no hosted model, no foundation model API calls, no data leaving your control beyond this single bounded call. Ideal for prototyping, evaluation, or low-volume use cases where a naive-Bayes local classifier is sufficient and cost ($0.01 USDC) matters. Prefer alternatives if you need deep-learning accuracy, large-scale inference, or topic discovery on unlabelled data.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T19:07:08.527Z","isFirstParty":false,"canonicalSlug":"meeting-topic-local-text-classifier-naive-bayes-09b6a4ff"}