👑 GLiNER.js: Generalist and Lightweight Named Entity Recognition for JavaScript

GLiNER.js is a TypeScript-based inference engine for running GLiNER (Generalist and Lightweight Named Entity Recognition) models. GLiNER can identify any entity type using a bidirectional transformer encoder, offering a practical alternative to traditional NER models and large language models.

📄 Paper
  •  
📢 Discord
  •  
🤗 Demo
  •  
🤗 Available models
  •  
🧬 Official Repo

🌟 Key Features

- Flexible entity recognition without predefined categories
- Lightweight and fast inference
- Easy integration with web applications
- TypeScript support for better developer experience

🚀 Getting Started

$3

``bash
npm install gliner
`

$3

`javascript
const gliner = new Gliner({
tokenizerPath: "onnx-community/gliner_small-v2",
onnxSettings: {
modelPath: "public/model.onnx", // Can be a string path or Uint8Array/ArrayBufferLike
executionProvider: "webgpu", // Optional: "cpu", "wasm", "webgpu", or "webgl"
wasmPaths: "path/to/wasm", // Optional: path to WASM binaries
multiThread: true, // Optional: enable multi-threading (for wasm/cpu providers)
maxThreads: 4, // Optional: specify number of threads (for wasm/cpu providers)
fetchBinary: true, // Optional: prefetch binary from wasmPaths
},
transformersSettings: {
// Optional
allowLocalModels: true,
useBrowserCache: true,
},
maxWidth: 12, // Optional
modelType: "gliner", // Optional
});

await gliner.initialize();

const texts = ["Your input text here"];
const entities = ["city", "country", "person"];
const options = {
flatNer: false, // Optional
threshold: 0.1, // Optional
multiLabel: false, // Optional
};

const results = await gliner.inference({
texts,
entities,
...options,
});
console.log(results);
`

$3

The inference results will be returned in the following format:

`javascript
// For a single text input:
[
{
spanText: "New York", // The extracted entity text
start: 10, // Start character position
end: 18, // End character position
label: "city", // Entity type
score: 0.95, // Confidence score
},
// ... more entities
];

// For multiple text inputs, you'll get an array of arrays
`

🛠 Setup & Model Preparation

To use GLiNER models in a web environment, you need an ONNX format model. You can:

1. Search for pre-converted models on HuggingFace
2. Convert a model yourself using the official Python script

$3

Use the convert_to_onnx.py script with the following arguments:

- model_path: Location of the GLiNER model
- save_path: Where to save the ONNX file
- quantize: Set to True for IntU8 quantization (optional)

Example:

`bash
python convert_to_onnx.py --model_path /path/to/your/model --save_path /path/to/save/onnx --quantize True
`

🌟 Use Cases

GLiNER.js offers versatile entity recognition capabilities across various domains:

1. Enhanced Search Query Understanding
2. Real-time PII Detection
3. Intelligent Document Parsing
4. Content Summarization and Insight Extraction
5. Automated Content Tagging and Categorization
...

🔧 Areas for Improvement

- [ ] Further optimize inference speed
- [ ] Add support for token-based GLiNER architecture
- [ ] Implement bi-encoder GLiNER architecture for better scalability
- [ ] Enable model training capabilities
- [ ] Provide more usage examples

Creating a PR

- for any changes, remember to run pnpm changeset`, otherwise there will not be a version bump and the PR Github Action will fail.

🙏 Acknowledgements

- GLiNER original authors
- ONNX Runtime Web
- Transformers.js

📞 Support

For questions and support, please join our Discord community or open an issue on GitHub.