gliner4j-gliner2-base-v1

GLiNER4j ONNX — GLiNER2 Base

ONNX export of fastino/gliner2-base-v1 for Java inference via ONNX Runtime.

Part of the GLiNER4j project.

Supported Tasks

Both tasks support entity/label descriptions for improved accuracy and per-call overrides without model reloading.

Repository Structure

├── gliner4j_config.json        # Shared model configuration
├── tokenizer.json              # Shared HuggingFace tokenizer
├── tokenizer_config.json
├── onnx/                       # Base FP32 (~830 MB)
│   ├── ner_full.onnx
│   └── classifier_full.onnx
├── onnx_fp16/                  # FP16 (~416 MB, ~50% smaller)
│   ├── ner_full.onnx
│   └── classifier_full.onnx
└── onnx_quantized/             # INT8 dynamic quantization (~208 MB, ~75% smaller)
    ├── ner_full.onnx
    └── classifier_full.onnx

An onnx_optimized_cpu/ folder with the same two files may also be present (ONNX Runtime graph-optimized for CPU).

Model Architecture

Each variant ships two merged, self-contained ONNX graphs — one per task:

The graphs are fused at export time from the encoder and task heads; the intermediate split modules are not published.

Variants

To download a specific variant only:

huggingface-cli download <repo> --include "onnx_fp16/*" "*.json"

Configuration

Usage

Use with GLiNER4j, a Java library for GLiNER2 inference via ONNX Runtime.

Named Entity Recognition

var entities = List.of(
    new EntityDefinition("person", "Names of individuals"),
    new EntityDefinition("organization", "Company or institution names")
);
var gliner = GLiNER4jNER.load(modelDir, entities);
Map<String, List<EntitySpan>> results = gliner.extract("John works at Google.");

Text Classification

var labels = List.of(
    new ClassificationLabel("positive", "Expresses positive sentiment"),
    new ClassificationLabel("negative", "Expresses negative sentiment")
);
var classifier = GLiNER4jClassifier.load(modelDir, labels);
List<ClassificationResult> results = classifier.classify("Great product!");

Model Variants

// FP16 variant
var gliner = GLiNER4jNER.load(modelDir, entities, "onnx_fp16");

// Quantized variant
var gliner = GLiNER4jNER.load(modelDir, entities, "onnx_quantized");

License

Apache License 2.0