Add/update the quantized ONNX model files and README.md for Transformers.js v3 (#1)

- Add/update the quantized ONNX model files and README.md for Transformers.js v3 (d28f4d7ed728695ccf5611b8fb2905e5edbc256c)


Co-authored-by: Yuichiro Tachibana <[email protected]>

README.md

@@ -6,4 +6,23 @@ pipeline_tag: zero-shot-classification
 
 https://huggingface.co/cross-encoder/nli-deberta-base 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/nli-deberta-base');
+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`).

onnx/model_bnb4.onnx

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onnx/model_int8.onnx

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onnx/model_q4.onnx

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onnx/model_q4f16.onnx

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onnx/model_uint8.onnx

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