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library_name: "transformers.js" |
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--- |
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https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english with ONNX weights to be compatible with Transformers.js. |
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## Usage (Transformers.js) |
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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: |
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```bash |
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npm i @xenova/transformers |
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``` |
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You can then use the model to classify text like this: |
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```js |
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import { pipeline } from "@xenova/transformers"; |
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// Create a sentiment analysis pipeline |
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const classifier = await pipeline('sentiment-analysis', 'Xenova/distilbert-base-uncased-finetuned-sst-2-english'); |
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// Classify input text |
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const output = await classifier('I love transformers!'); |
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console.log(output); |
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// [{ label: 'POSITIVE', score: 0.999788761138916 }] |
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// Classify input text (and return all classes) |
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const output2 = await classifier('I love transformers!', { topk: null }); |
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console.log(output2); |
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// [ |
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// { label: 'POSITIVE', score: 0.999788761138916 }, |
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// { label: 'NEGATIVE', score: 0.00021126774663571268 } |
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// ] |
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``` |
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--- |
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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`). |
