Relations & Classification

GLiREL and GLiClass models extract relationships and classify text with zero-shot label support.

Relation Extraction

GLiREL models extract relationships between entities. Relation types are passed via the labels parameter. Entities must be pre-extracted (e.g. with GLiNER) and passed in item.metadata:

Python

from sie_sdk import SIEClient
from sie_sdk.types import Item

client = SIEClient("http://localhost:8080")

text = "Tim Cook is the CEO of Apple Inc."

# Step 1: Extract entities with GLiNER
ner_result = client.extract(
    "urchade/gliner_multi-v2.1",
    Item(text=text),
    labels=["person", "organization"]
)

# Step 2: Pass entities to GLiREL for relation extraction
result = client.extract(
    "jackboyla/glirel-large-v0",
    Item(text=text, metadata={"entities": ner_result["entities"]}),
    labels=["works_for", "ceo_of", "founded"]
)

for relation in result["relations"]:
    print(f"{relation['head']} --{relation['relation']}--> {relation['tail']}")
# Tim Cook --ceo_of--> Apple Inc.

TypeScript

import { SIEClient } from "@superlinked/sie-sdk";

const client = new SIEClient("http://localhost:8080");

const text = "Tim Cook is the CEO of Apple Inc.";

// Step 1: Extract entities with GLiNER
const nerResult = await client.extract(
  "urchade/gliner_multi-v2.1",
  { text },
  { labels: ["person", "organization"] }
);

// Step 2: Pass entities to GLiREL for relation extraction
const result = await client.extract(
  "jackboyla/glirel-large-v0",
  { text, metadata: { entities: nerResult.entities } },
  { labels: ["works_for", "ceo_of", "founded"] }
);

for (const relation of result.relations) {
  console.log(`${relation.head} --${relation.relation}--> ${relation.tail}`);
}
// Tim Cook --ceo_of--> Apple Inc.

await client.close();

Relation Fields

Field	Type	Description
head	str	Source entity
tail	str	Target entity
relation	str	Relation type
score	float	Confidence score

Text Classification

GLiClass models classify text into categories:

Python

result = client.extract(
    "knowledgator/gliclass-base-v1.0",
    Item(text="I absolutely loved this movie! The acting was superb."),
    labels=["positive", "negative", "neutral"]
)

for classification in result["classifications"]:
    print(f"{classification['label']}: {classification['score']:.2f}")
# positive: 0.94
# neutral: 0.04
# negative: 0.02

TypeScript

const result = await client.extract(
  "knowledgator/gliclass-base-v1.0",
  { text: "I absolutely loved this movie! The acting was superb." },
  { labels: ["positive", "negative", "neutral"] }
);

for (const classification of result.classifications) {
  console.log(`${classification.label}: ${classification.score.toFixed(2)}`);
}
// positive: 0.94
// neutral: 0.04
// negative: 0.02

Overflow policy

GLiClass models (gliclass-{small,base,large}-v1.0) have a 512-token fused context; text and the <<LABEL>>...<<SEP>> label prompt share the same budget. When input exceeds that budget, the underlying gliclass library can crash or return corrupted output. Set overflow_policy per request to control how the server handles overflow before it reaches the library.

overflow_policy is gliclass-only today. See the Python SDK and TypeScript SDK references for the typed exception, and the HTTP API for the wire-level option.

Value	Behavior on overflow
default (server default)	Input passes through as-is. Not recommended for production; use only to reproduce public benchmarks.
truncate_text	End of text is truncated to fit; labels are preserved. Raises INPUT_TOO_LONG only if the label prompt alone exceeds the context.
error	Always raises INPUT_TOO_LONG on overflow.

Python

from sie_sdk import SIEClient
from sie_sdk.client.errors import InputTooLongError
from sie_sdk.types import Item

client = SIEClient("http://localhost:8080")

try:
    result = client.extract(
        "knowledgator/gliclass-small-v1.0",
        Item(text=long_review),
        labels=["positive", "negative", "neutral"],
        options={"overflow_policy": "truncate_text"},
    )
except InputTooLongError as e:
    print(f"Input too long for {e.model}: {e}")

TypeScript

import { SIEClient, InputTooLongError } from "@superlinked/sie-sdk";

const client = new SIEClient("http://localhost:8080");

try {
  const result = await client.extract(
    "knowledgator/gliclass-small-v1.0",
    { text: longReview },
    {
      labels: ["positive", "negative", "neutral"],
      adapterOptions: { overflow_policy: "truncate_text" },
    },
  );
} catch (error) {
  if (error instanceof InputTooLongError) {
    console.log(`Input too long for ${error.model}: ${error.message}`);
  }
}

Recommended Models

Relation Extraction Models

Model	Notes
jackboyla/glirel-large-v0	Zero-shot relation extraction

NER Models

Model	Languages	Notes
urchade/gliner_multi-v2.1	Multilingual	General-purpose NER
urchade/gliner_large-v2.1	English	Larger model (459M params)
numind/NuNER_Zero	English	Zero-shot NER
urchade/gliner_multi_pii-v1	Multilingual	PII detection

Classification Models

Model	Notes
knowledgator/gliclass-base-v1.0	Zero-shot classification
knowledgator/gliclass-small-v1.0	Faster, smaller

See Full model catalog for the complete list.

Framework Integrations

All extraction types (entities, relations, classifications) are also available through framework integrations. See LangChain, LlamaIndex, Haystack, DSPy, and CrewAI for framework-specific examples.

What’s Next

• NER & Entity Extraction - named entity recognition
• Vision Tasks - image captioning, object detection, and document understanding
• OCR - convert document images and PDFs to Markdown
• Full model catalog - all supported models