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gliner2-base-v1 is a versatile extraction-and-classification model for the structured tasks that fill most production pipelines. Point it at any text and, with a single API call, pull named entities by your own labels, populate a typed JSON schema straight from messy input, or classify by sentiment, intent, or topic. No fine-tuning and no prompt engineering, just a label set or schema at inference time. Because it’s purpose-built and CPU-optimized, it runs faster and cheaper than routing this work to a general-purpose frontier model. Reach for gliner-multi-pii-v1 when the job is dedicated PII redaction. When you need clean structure out of raw text, this is the model.
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:gliner2-base-v1
required

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

Allowed value: "gliner2-base-v1"
Example:

"gliner2-base-v1"

input
string<textarea>
required

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

Required string length: 1 - 131072
metadata
object

Use-case options for the model. Three use cases are supported:

  • ner — extract entities for the given labels.
  • json — extract structured fields defined by a schema.
  • classification — assign labels from candidate sets defined by a schema.

Response

Success

The response is of type object.