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": "gliner2.5-base-v1"
}
'{}{}gliner2.5-base-v1
Model details for gliner2.5-base-v1. English schema-based information extraction built on DeBERTa-v3-base.
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": "gliner2.5-base-v1"
}
'{}{}Fastino’s GLiNER2.5 Base is an English schema-based information extraction model built on DeBERTa-v3-base. A single model covers entity extraction, text classification, structured record extraction, relation extraction, and span-level attributes — you supply a label set or a schema at inference time instead of fine-tuning one model per task. It fits GTM workflows such as parsing inbound leads, extracting company and contact details, classifying sales conversations, identifying relationships between people and companies, structuring CRM notes, and enriching customer or account data.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.
"gliner2.5-base-v1""gliner2.5-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.