GLiNER relex Large
An open extraction model that pulls entities, and the relations between them, out of text. knowledgator/
- Accuracy
FewRel, F1 · not measured
- Time per request
Not timed one request at a time
- Serverless price
$0.22
per 1M tokens
-
Build knowledge graphs on par with Claude Sonnet 5.5, 99% cheaper
110
Relations right · Claude Sonnet 5.5 = 100
See the comparison on Knowledge graph$48
Cost per 1M articles
Call it
from sie_sdk import SIEClientclient = SIEClient(api_key="API keysk-sie-…")text = "textDavid Michael Draiman (born March 13, 1973) is an American singer who is best known as the lead vocalist of the band···named "The Vengeful One". They produced it over a year before, and along with it announced a new album, Immortalized."result = client.extract( "modelknowledgator/gliner-relex-large-v1.0", {"text": text}, labels=[ "person", "organization", "location", "date", "work, product or event", ], options={ "relation_labels": [ "country of citizenship", "notable work", "part of", "performer", "publication date", "participant of", "member of", "country of origin", "date of birth", "cast member", "inception", "record label", ], "threshold": 0.3, "relation_threshold": 0.7, },)for relation in result["relations"]: print(relation["head"], relation["relation"], relation["tail"])import { SIEClient } from '@superlinked/sie-sdk';
const client = new SIEClient(undefined, { apiKey: 'sk-sie-…' });
const text = "David Michael Draiman (born March 13, 1973) is an American singer who is best known as the lead vocalist of the band···named \"The Vengeful One\". They produced it over a year before, and along with it announced a new album, Immortalized.";
const result = await client.extract(
'knowledgator/gliner-relex-large-v1.0',
{ text },
{
labels: [
"person", "organization", "location", "date",
"work, product or event",
],
adapterOptions: {
relation_labels: [
"country of citizenship", "notable work",
"part of", "performer", "publication date",
"participant of", "member of",
"country of origin", "date of birth",
"cast member", "inception", "record label",
],
threshold: 0.3,
relation_threshold: 0.7,
},
},
);
for (const relation of result.relations) {
console.log(relation.head, relation.relation, relation.tail);
}curl https://api.superlinked.com/v1/extract/knowledgator%2Fgliner-relex-large-v1.0 \
-H "Authorization: Bearer sk-sie-…" \
-H "Content-Type: application/json" \
-d "{\"items\":[{\"text\":\"David Michael Draiman (born March 13, 1973) is an American singer who is best known as the lead vocalist of the band···\\\"The Vengeful One\\\". They produced it over a year before, and along with it announced a new album, Immortalized.\"}],\"params\":{\"labels\":[\"person\",\"organization\",\"location\",\"date\",\"work, product or event\"],\"options\":{\"relation_labels\":[\"country of citizenship\",\"notable work\",\"part of\",\"performer\",\"publication date\",\"participant of\",\"member of\",\"country of origin\",\"date of birth\",\"cast member\",\"inception\",\"record label\"],\"threshold\":0.3,\"relation_threshold\":0.7}}}"Build the "Knowledge graph" capability into my app using the Superlinked Inference Engine (SIE).
Context
- SIE is an OpenAI-style inference API. Python SDK: `from sie_sdk import SIEClient`; TypeScript: `@superlinked/sie-sdk`.
- Base URL: https://api.superlinked.com (or my regional endpoint). Auth: Bearer key from env `SIE_API_KEY` (never hard-code it).
- Model: knowledgator/gliner-relex-large-v1.0 (SIE primitive: /extract). Keep the model id configurable.
Task
- Input: a block of text.
- Behaviour: return the relations the text states between entities of the named types, each as its head, relation type and tail
- Send the one /extract call shown in the SDK snippet: the entity types as labels and the relation types in options.relation_labels, with the thresholds it names. Read each relation's head, relation and tail from the returned item's relations.
Deliverables
- A typed client wrapper, an application-level function for this task, error handling for timeouts/empty input, and unit tests with a stubbed client.
- Wire it into my existing stack (ask me which framework if unclear) and add a short usage example. Edit the code and run it live Sign up to run this example.
Get started no credit card required
Next to the other relation models
| Model | FewRel, F1 | Time per request | Serverless price | Size |
|---|---|---|---|---|
| GLiNER relex Large knowledgator | Not measured | Not timed | $0.22 per 1M tokens | 467M |
| gliner2 large v1 fastino | 0.435 | Not timed | $0.186 per 1M tokens | 486M |
| gliner2 base v1 fastino | 0.400 | Not timed | $0.0929 per 1M tokens | 208M |
| glirel large v0 jackboyla | 0.264 | Not timed | Run in your cloud | 467M |
Facts
- Size
- 467M parameters
- License
- Apache-2.0
- Reads
- Text, up to 2,048 tokens
- Endpoint
-
extract
Evaluation
- Accuracy
- Run on SIE against the public dataset named in each figure
- Prices
- Serverless list prices
- Sources
- Task page runs and their inputs
Run GLiNER relex Large on Serverless or in your own cloud
SIE Serverless
We host the models; you call one API.
- No idle GPUs, pay for what you use
- Fits your stack: SDK, API, CLI, MCP
- Self-host the same open-source engine
- SOC 2 Type 2, US or EU data residency
- Create an account to try Serverless
SIE Cluster
Kubernetes in your own cloud, backed by our engineers.
- Terraform modules for your cloud
- Apache-2.0, same engine as Serverless
- GPU workers scale to zero between jobs
- Separate worker pools for each tenant
Agent prompt
Deploy SIE to our AWS account with the superlinked/sie/aws Terraform module. Docs: superlinked.com/docs/deployment.Deploy SIE to our Google Cloud project with the superlinked/sie/google Terraform module. Docs: superlinked.com/docs/deployment.Deploy SIE to our Azure subscription with the superlinked/sie/azure Terraform module. Docs: superlinked.com/docs/deployment.Deploy SIE to our Alibaba Cloud account with the superlinked/sie/alicloud Terraform module. Docs: superlinked.com/docs/deployment. SIE Local
The open-source server on your own machine.
- NVIDIA
- One container, no cluster
- Process your data fully offline
- Same SDK and model IDs as Serverless
docker run --gpus all -p 8080:8080 ghcr.io/superlinked/sie-server:latest-cuda12-default