README.md

---
library_name: transformers.js
base_model: NbAiLab/nb-sbert-base
tags:
- onnx
- transformers.js
- feature-extraction
- sentence-similarity
language:
- no
pipeline_tag: feature-extraction
license: apache-2.0
---

# ONNX version of NbAiLab/nb-sbert-base

This repository contains **ONNX-converted weights** for [NbAiLab/nb-sbert-base](https://huggingface.co/NbAiLab/nb-sbert-base), compatible with [Transformers.js](https://huggingface.co/docs/transformers.js).

It includes both:
1.  **Quantized (int8):** Faster, smaller (default).
2.  **Full Precision (float32):** Higher theoretical accuracy, larger file size.

## Usage (Node.js)

First, install the library:

```bash
npm install @huggingface/transformers
```

## Option 1: Use Quantized Model (Recommended)
This is the default behavior. It loads model_quantized.onnx (approx. 4x smaller).

```javascript
import { pipeline } from '@huggingface/transformers';

const embedder = await pipeline(
  'feature-extraction', 
  'lebchen/nb-sbert-base-onnx', 
  { device: 'auto' } // Defaults to { quantized: true }
);

const output = await embedder("Dette er en test.", { pooling: 'mean', normalize: true });
```

## Option 2: Use Full Precision Model

```
import { pipeline } from '@huggingface/transformers';

const embedder = await pipeline(
  'feature-extraction', 
  'lebchen/nb-sbert-base-onnx', 
  { 
    device: 'auto', 
    quantized: false // Forces loading of model.onnx
  }
);
```

## Credits & License

The original model was developed by the National Library of Norway (AI Lab). Original repository: https://huggingface.co/NbAiLab/nb-sbert-base

This distribution is licensed under Apache 2.0.