curl --request POST \
--url https://api.zerogpu.ai/v1/responses \
--header 'Content-Type: application/json' \
--header 'x-api-key: <api-key>' \
--data '
{
"input": "The application is built with Python 3.11 and uses PostgreSQL 15 for storage. It runs on Kubernetes with Docker containers and communicates via gRPC.",
"model": "gliner2-base-v1"
}
'{
"entities": {
"programming language": [
"Python 3.11"
],
"database": [
"PostgreSQL 15"
],
"technology": [
"Kubernetes",
"Docker",
"gRPC"
],
"protocol": [
"gRPC"
]
}
}gliner2-base-v1
Model details for gliner2-base-v1.
curl --request POST \
--url https://api.zerogpu.ai/v1/responses \
--header 'Content-Type: application/json' \
--header 'x-api-key: <api-key>' \
--data '
{
"input": "The application is built with Python 3.11 and uses PostgreSQL 15 for storage. It runs on Kubernetes with Docker containers and communicates via gRPC.",
"model": "gliner2-base-v1"
}
'{
"entities": {
"programming language": [
"Python 3.11"
],
"database": [
"PostgreSQL 15"
],
"technology": [
"Kubernetes",
"Docker",
"gRPC"
],
"protocol": [
"gRPC"
]
}
}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 docs • Terms • Privacy
Authorizations
Headers
Optional project identifier. Scopes the request to a specific project when provided.
Body
Model identifier (fixed for this playground). Use request examples to change use cases.
"gliner2-base-v1""gliner2-base-v1"
Multi-line text or document content to send to the model.
1 - 131072Use-case options for the model. Three use cases are supported:
ner— extract entities for the givenlabels.json— extract structured fields defined by aschema.classification— assign labels from candidate sets defined by aschema.
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Which operation to run and which other fields apply: ner uses labels; json and classification use schema.
ner, json, classification Entity types to extract, used when usecase is ner. Each label becomes a key in the returned entities object.
[
"programming language",
"database",
"technology",
"protocol"
]
Minimum confidence score (0–1) an entity must reach to be returned (used with ner). Lower values surface more, lower-confidence matches.
0 <= x <= 1Extraction/classification definition, used when usecase is json or classification. For json, each group maps to field definitions in name::type::description form (e.g. "email::str::Email address"). For classification, each group maps to a list of candidate labels (e.g. ["positive", "negative", "neutral"]).
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{
"contact": [
"name::str::Full name",
"email::str::Email address",
"phone::str::Phone number"
]
}
Response
Success
The response is of type object.