| base_model: typeform/distilbert-base-uncased-mnli | |
| library_name: transformers.js | |
| pipeline_tag: zero-shot-classification | |
| https://huggingface.co/typeform/distilbert-base-uncased-mnli 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 | |
| ``` | |
| **Example:** Zero-shot classification. | |
| ```js | |
| import { pipeline } from '@huggingface/transformers'; | |
| const classifier = await pipeline('zero-shot-classification', 'Xenova/distilbert-base-uncased-mnli'); | |
| const output = await classifier( | |
| 'I love transformers!', | |
| ['positive', 'negative'] | |
| ); | |
| ``` | |
| 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`). |