{"uid":"cap_1u41SMJwliJfDFseYxEAj","slug":"language-routing-multiclass-log-loss-calculator-0ac7f9f3","name":"Language Routing Multiclass Log Loss Calculator","description":"Language routing - Multiclass log loss: Multiclass log loss for Language routing. 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 multiclass log loss deliverable.","url":"https://market.datapackvibe.com/x402/assistant-sector-ml-language-routing-log-loss?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"records":{"type":"array","items":{"type":"object","required":["id","text","actual","predicted","probabilities"],"properties":{"id":{"type":"string","default":"","maxLength":80,"minLength":1},"text":{"type":"string","default":"","maxLength":1000},"actual":{"enum":["english","french","spanish","other"],"type":"string"},"predicted":{"enum":["english","french","spanish","other"],"type":"string"},"probabilities":{"type":"array","items":{"type":"number","default":0,"maximum":1,"minimum":0},"default":[],"maxItems":4,"minItems":4}},"additionalProperties":false},"default":[{"id":"example-0-0","text":"english sample document number 0 for this supplied dataset.","actual":"english","predicted":"english","probabilities":[0.8,0.06666666666666667,0.06666666666666667,0.06666666666666667]},{"id":"example-0-1","text":"english sample document number 1 for this supplied dataset.","actual":"english","predicted":"english","probabilities":[0.8,0.06666666666666667,0.06666666666666667,0.06666666666666667]},{"id":"example-0-2","text":"english sample document number 2 for this supplied dataset.","actual":"english","predicted":"english","probabilities":[0.8,0.06666666666666667,0.06666666666666667,0.06666666666666667]},{"id":"example-1-0","text":"french sample document number 0 for this supplied dataset.","actual":"french","predicted":"french","probabilities":[0.06666666666666667,0.8,0.06666666666666667,0.06666666666666667]},{"id":"example-1-1","text":"french sample document number 1 for this supplied dataset.","actual":"french","predicted":"french","probabilities":[0.06666666666666667,0.8,0.06666666666666667,0.06666666666666667]},{"id":"example-1-2","text":"french sample document number 2 for this supplied dataset.","actual":"french","predicted":"french","probabilities":[0.06666666666666667,0.8,0.06666666666666667,0.06666666666666667]},{"id":"example-2-0","text":"spanish sample document number 0 for this supplied dataset.","actual":"spanish","predicted":"spanish","probabilities":[0.06666666666666667,0.06666666666666667,0.8,0.06666666666666667]},{"id":"example-2-1","text":"spanish sample document number 1 for this supplied dataset.","actual":"spanish","predicted":"spanish","probabilities":[0.06666666666666667,0.06666666666666667,0.8,0.06666666666666667]},{"id":"example-2-2","text":"spanish sample document number 2 for this supplied 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multiclass log loss metric for language routing model predictions across English, French, Spanish, and other language classes from up to 200 labeled text records.","exampleAgentPrompt":"Compute the multiclass log loss for my language routing model using these 50 labeled records — each has the text, the actual language (english, french, spanish, or other), the predicted label, and the 4-class probability vector my model output.","exampleUseCases":[{"title":"Language classifier calibration audit","prompt":"I have 150 labeled documents with actual and predicted language labels (english, french, spanish, other) plus the probability outputs from my routing model — can you compute the multiclass log loss so I can see how well-calibrated the predictions are?"},{"title":"Comparing two language routing models","prompt":"I want to evaluate two versions of my language detector. Here are 80 labeled records from model A with actual classes, predicted classes, and 4-class probability arrays — compute the multiclass log loss so I can compare it against model B's score."},{"title":"Language model QA before deployment","prompt":"Before we ship this language routing update, run multiclass log loss on these 200 labeled test records — each has the text, actual language label, predicted label, and the four probability scores for english, french, spanish, and other."}],"resultDescription":"Returns a bounded multiclass log loss value computed over the supplied labeled records, reflecting how well the model's predicted probability distributions match the actual language classes across English, French, Spanish, and other categories.","failureModes":["Probability vectors that don't sum to 1 may cause computation errors or inaccurate results","Records with fewer or more than 4 probability values (one per class) will be rejected","Exceeding the 200 record limit per call will result in an error","Missing required fields (id, text, actual, predicted, probabilities) on any record will cause validation failure","Payment of 0.01 USDC must be provided via x402 protocol or call will be rejected"],"whenToPreferThis":"Choose this endpoint when you need a quick, inexpensive computation of multiclass log loss for a language routing or language detection classifier with up to 4 classes (English, French, Spanish, other) and up to 200 records per batch, without needing to host or train a model. It is ideal for evaluation-only workflows where you already have model predictions and probability outputs and simply want the standard cross-entropy loss metric.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T18:47:39.355Z","isFirstParty":false,"canonicalSlug":"language-routing-multiclass-log-loss-calculator-0ac7f9f3"}