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Sponsored by
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knowledgator / gliner-linker-large-v1.0
Model's Last Updated: February 24 2026
token-classification
Introduction of gliner-linker-large-v1.0
Model Details of gliner-linker-large-v1.0
GLiNER-Linker: Entity Disambiguation Model
Bi-encoder model for entity disambiguation - the neural component (Layer 3) of the GLiNKER framework .
Models
| Model | Base Encoder | Use Case |
|---|---|---|
| gliner-linker-base-v1.0 | deberta-v3-base | Balanced performance |
| gliner-linker-large-v1.0 | deberta-v3-large | Maximum accuracy |
| gliner-linker-rerank-v1.0 | ettin-encoder-68m | Reranking |
Both text encoder and label encoder use the same DeBERTa model
Quick Start with GLiNKER
pip install git+https://github.com/Knowledgator/GLinker.git
from glinker import ConfigBuilder, DAGExecutor
# Build pipeline
builder = ConfigBuilder(name="entity_linking")
# L1: Extract mentions
builder.l1.gliner(
model="knowledgator/gliner-bi-base-v2.0",
labels=["person", "organization", "location"]
)
# L2: Candidate retrieval
builder.l2.add("dict", priority=0)
# L3: Disambiguation with GLiNER-Linker
builder.l3.configure(
model="knowledgator/gliner-linker-large-v1.0",
use_precomputed_embeddings=True
)
# Execute
executor = DAGExecutor(builder.get_config())
executor.load_entities("entities.jsonl", target_layers=["dict"])
result = executor.execute({
"texts": ["Apple announced new iPhone"]
})
# Get linked entities
l0_result = result.get("l0_result")
for entity in l0_result.entities:
if entity.linked_entity:
print(f"{entity.mention_text} → {entity.linked_entity.label}")
print(f" Score: {entity.linked_entity.score:.3f}")
Precomputed Embeddings for Speed
Enable precomputed embeddings in L2 for 10-100× speedup:
builder.l2.embeddings(
enabled=True,
model_name="knowledgator/gliner-linker-large-v1.0"
)
# Precompute embeddings when loading entities
executor.load_entities("entities.jsonl", target_layers=["dict"])
executor.precompute_embeddings(target_layers=["postgres"], batch_size=8)
Set up Reranker
builder = ConfigBuilder(name="reranked")
builder.l1.gliner(model="knowledgator/gliner-bi-base-v2.0", labels=["gene", "disease"])
builder.l3.configure(model="knowledgator/gliner-linker-base-v1.0")
builder.l4.configure(
model="knowledgator/gliner-linker-rerank-v1.0",
threshold=0.3,
max_labels=5,
)
builder.save("config.yaml") # Generates L1 → L2 → L3 → L4 → L0
Entity Format (JSONL)
{"entity_id": "Q312", "label": "Apple Inc.", "description": "American technology company", "entity_type": "organization"}
{"entity_id": "Q89", "label": "Apple", "description": "Edible fruit of apple tree", "entity_type": "food"}
Resources
- GLiNKER Framework : GitHub
- Documentation : GLiNKER Docs
- Discord : Join Community
Citation
@misc{stepanov2024glinermultitask,
title={GLiNER multi-task: Generalist Lightweight Model for Various Information Extraction Tasks},
author={Ihor Stepanov and Mykhailo Shtopko},
year={2024},
eprint={2406.12925},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
Developed by Knowledgator
Runs of knowledgator gliner-linker-large-v1.0 on huggingface.co
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More Information About gliner-linker-large-v1.0 huggingface.co Model
Url of gliner-linker-large-v1.0
gliner-linker-large-v1.0 huggingface.co Url
Provider of gliner-linker-large-v1.0 huggingface.co
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