README.md

---
base_model: jinaai/jina-embeddings-v2-base-zh
language:
- zh
- en
library_name: transformers.js
license: apache-2.0
tags:
- feature-extraction
- sentence-similarity
- mteb
- sentence_transformers
- transformers
inference: false
---

https://huggingface.co/jinaai/jina-embeddings-v2-base-zh with ONNX weights to be compatible with Transformers.js.

## Usage (Transformers.js)

If you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@huggingface/transformers) using:
```bash
npm i @huggingface/transformers
```

You can then use the model to compute embeddings, as follows:

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

// Create a feature extraction pipeline
const extractor = await pipeline('feature-extraction', 'Xenova/jina-embeddings-v2-base-zh', {
    dtype: "fp32"  // Options: "fp32", "fp16", "q8", "q4"
});

// Compute sentence embeddings
const texts = ['How is the weather today?', '今天天气怎么样?'];
const output = await extractor(texts, { pooling: 'mean', normalize: true });
// Tensor {
//   dims: [2, 768],
// 	 type: 'float32',
//   data: Float32Array(1536)[...],
// 	 size: 1536
// }

// Compute cosine similarity between the two embeddings
const score = cos_sim(output[0].data, output[1].data);
console.log(score);
// 0.7860610759096025
```

---

Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).