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

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- Add/update the quantized ONNX model files and README.md for Transformers.js v3 (b84c2ea3b06dcef69362e87b66f98335756d1f81)


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

README.md CHANGED
@@ -7,15 +7,15 @@ https://huggingface.co/YituTech/conv-bert-base with ONNX weights to be compatibl
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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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  **Example:** Feature extraction w/ `Xenova/conv-bert-base`.
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  ```javascript
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- import { pipeline } from '@xenova/transformers';
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  // Create feature extraction pipeline
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  const extractor = await pipeline('feature-extraction', 'Xenova/conv-bert-base');
@@ -33,5 +33,4 @@ console.log(output)
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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`).
 
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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/@huggingface/transformers) using:
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  ```bash
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+ npm i @huggingface/transformers
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  ```
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  **Example:** Feature extraction w/ `Xenova/conv-bert-base`.
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  ```javascript
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+ import { pipeline } from '@huggingface/transformers';
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  // Create feature extraction pipeline
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  const extractor = await pipeline('feature-extraction', 'Xenova/conv-bert-base');
 
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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`).
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