| base_model: jinaai/jina-embeddings-v2-small-en | |
| library_name: transformers.js | |
| pipeline_tag: feature-extraction | |
| https://huggingface.co/jinaai/jina-embeddings-v2-small-en with ONNX weights to be compatible with Transformers.js. | |
| ## Usage with 🤗 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 | |
| ``` | |
| ```js | |
| import { pipeline, cos_sim } from '@huggingface/transformers'; | |
| // Create feature extraction pipeline | |
| const extractor = await pipeline('feature-extraction', 'Xenova/jina-embeddings-v2-small-en', | |
| { quantized: false } // Comment out this line to use the quantized version | |
| ); | |
| // Generate embeddings | |
| const output = await extractor( | |
| ['How is the weather today?', 'What is the current weather like today?'], | |
| { pooling: 'mean' } | |
| ); | |
| // Compute cosine similarity | |
| console.log(cos_sim(output[0].data, output[1].data)); // 0.9399812684139274 (unquantized) vs. 0.9341121503699659 (quantized) | |
| ``` | |
| 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`). |