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deberta-v3-small: Responses
Microsoft’s DeBERTa-v3-small is a fast, lightweight zero-shot text classifier for high-volume routing, filtering, and tagging. Hand it any text alongside your own candidate labels and it returns a confidence score for each. No training data and no fixed taxonomy, just the categories you define at request time. Because it scores every label rather than forcing one choice, you get soft probabilities to threshold, rank, or route on however your pipeline needs. Purpose-built and CPU-optimized, it runs faster and cheaper than asking a general-purpose frontier model to classify.
References: Model docsTermsPrivacy

Authorizations

x-api-key
string
header
required

Headers

x-project-id
string

Optional project identifier. Scopes the request to a specific project when provided.

Body

application/json
model
string
default:deberta-v3-small
required

Model identifier (fixed for this playground). Use request examples to change use cases.

Allowed value: "deberta-v3-small"
Example:

"deberta-v3-small"

input
string<textarea>
required

Multi-line text or document content to send to the model.

Required string length: 1 - 131072
instructions
string

Candidate categories for zero-shot classification, passed as a comma-separated array (e.g. [sports, finance, politics]). A maximum of 10 categories is supported; response time increases with the number of categories provided.

Example:

"[sports, finance, politics]"

Response

Success

The response is of type object.