⭐GLiClass.js: Generalist and Lightweight Model for Sequence Classification in JS

GLiClass.js is a TypeScript - based inference engine for running GLiClass(Generalist and Lightweight Model for Sequence Classification) models. This is an efficient zero-shot classifier inspired by GLiNER work. It demonstrates the same performance as a cross-encoder while being more compute-efficient because classification is done at a single forward path.

It can be used for topic classification, sentiment analysis and as a reranker in RAG pipelines.

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💫 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

Installation

npm install gliclass

Basic Usage

const gliclass = new Gliclass({
    tokenizerPath: "knowledgator/gliclass-small-v1.0",
    onnxSettings: {
      modelPath: "public/model.onnx",
      executionProvider: "cpu",
      multiThread: true,
    },
    promptFirst: false,
  });

  await gliclass.initialize();

const input_text = "Your input text here";
const texts = [input_text];
const labels = ["business", "science", "tech"];
const threshold = 0.5;

const decoded = await gliclass.inference({ texts, labels, threshold });
console.log(decoded);

Advanced Usage

ONNX settings API

• modelPath: can be either a URL to a local model as in the basic example, or it can also be the Model itself as an array of binary data.
• executionProvider: these are the same providers that ONNX web supports, currently we allow webgpu (recommended), cpu, wasm, webgl but more can be added
• wasmPaths: Path to the wasm binaries, this can be either a URL to the binaries like a CDN url, or a local path to a folder with the binaries.
• multiThread: wether to multithread at all, only relevent for wasm and cpu exeuction providers.
• multiThread: When choosing the wasm or cpu provider, multiThread will allow you to specify the number of cores you want to use.
• fetchBinary: will prefetch the binary from the default or provided wasm paths

🛠 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 Python convert_to_onnx.py.

Converting to ONNX Format

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:

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

🌟 Use Cases

GLiClass.js offers versatile text classification capabilities across various domains:

1. Documents Classification
2. Sentiment Analysis
3. Reranking of Search Results ...

🔧 Areas for Improvement

• Further optimize inference speed
• Add support for more architectures
• 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.