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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 (b73940d05c3dbd2307a3e19bff424663ed0417f3)


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

README.md CHANGED
@@ -7,14 +7,14 @@ https://huggingface.co/cross-encoder/ms-marco-TinyBERT-L-2-v2 with ONNX weights
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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:** Information Retrieval w/ `Xenova/ms-marco-TinyBERT-L-2-v2`.
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  ```js
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- import { AutoTokenizer, AutoModelForSequenceClassification } from '@xenova/transformers';
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  const model = await AutoModelForSequenceClassification.from_pretrained('Xenova/ms-marco-TinyBERT-L-2-v2');
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  const tokenizer = await AutoTokenizer.from_pretrained('Xenova/ms-marco-TinyBERT-L-2-v2');
@@ -31,7 +31,7 @@ const features = tokenizer(
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  }
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  )
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- const scores = await model(features)
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  console.log(scores);
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  // quantized: [ 7.210887908935547, -11.559350967407227 ]
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  // unquantized: [ 7.235750675201416, -11.562294006347656 ]
@@ -39,5 +39,4 @@ console.log(scores);
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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:** Information Retrieval w/ `Xenova/ms-marco-TinyBERT-L-2-v2`.
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  ```js
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+ import { AutoTokenizer, AutoModelForSequenceClassification } from '@huggingface/transformers';
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  const model = await AutoModelForSequenceClassification.from_pretrained('Xenova/ms-marco-TinyBERT-L-2-v2');
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  const tokenizer = await AutoTokenizer.from_pretrained('Xenova/ms-marco-TinyBERT-L-2-v2');
 
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  }
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  )
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+ const scores = await model(features);
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  console.log(scores);
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  // quantized: [ 7.210887908935547, -11.559350967407227 ]
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  // unquantized: [ 7.235750675201416, -11.562294006347656 ]
 
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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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