| base_model: YituTech/conv-bert-medium-small | |
| library_name: transformers.js | |
| https://huggingface.co/YituTech/conv-bert-medium-small 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/@xenova/transformers) using: | |
| ```bash | |
| npm i @xenova/transformers | |
| ``` | |
| **Example:** Feature extraction w/ `Xenova/conv-bert-medium-small`. | |
| ```javascript | |
| import { pipeline } from '@xenova/transformers'; | |
| // Create feature extraction pipeline | |
| const extractor = await pipeline('feature-extraction', 'Xenova/conv-bert-medium-small', { quantized: false }); | |
| // Perform feature extraction | |
| const output = await extractor('This is a test sentence.'); | |
| console.log(output) | |
| // Tensor { | |
| // dims: [ 1, 8, 384 ], | |
| // type: 'float32', | |
| // data: Float32Array(3072) [ 0.5316754579544067, 0.5101212859153748, ... ], | |
| // size: 3072 | |
| // } | |
| ``` | |
| --- | |
| 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`). |