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

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  1. README.md +16 -10
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  # text-embedding-ada-002 Tokenizer
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- A 🤗-compatible version of the **text-embedding-ada-002 tokenizer** (adapted from [openai/tiktoken](https://github.com/openai/tiktoken)). This means it can be used with Hugging Face libraries including [Transformers](https://github.com/huggingface/transformers), [Tokenizers](https://github.com/huggingface/tokenizers), and [Transformers.js](https://github.com/xenova/transformers.js).
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- ## Example usage:
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- ### Transformers/Tokenizers
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- ```py
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- from transformers import GPT2TokenizerFast
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-
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- tokenizer = GPT2TokenizerFast.from_pretrained('Xenova/text-embedding-ada-002')
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- assert tokenizer.encode('hello world') == [15339, 1917]
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  ```
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- ### Transformers.js
 
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  ```js
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- import { AutoTokenizer } from '@xenova/transformers';
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  const tokenizer = await AutoTokenizer.from_pretrained('Xenova/text-embedding-ada-002');
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  const tokens = tokenizer.encode('hello world'); // [15339, 1917]
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  ```
 
 
 
 
 
 
 
 
 
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  # text-embedding-ada-002 Tokenizer
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+ A 🤗-compatible version of the **text-embedding-ada-002 tokenizer** (adapted from [openai/tiktoken](https://github.com/openai/tiktoken)). This means it can be used with Hugging Face libraries including [Transformers](https://github.com/huggingface/transformers), [Tokenizers](https://github.com/huggingface/tokenizers), and [Transformers.js](https://github.com/huggingface/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/@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:** Tokenizer usage with Transformers.js
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+
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  ```js
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+ import { AutoTokenizer } from '@huggingface/transformers';
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  const tokenizer = await AutoTokenizer.from_pretrained('Xenova/text-embedding-ada-002');
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  const tokens = tokenizer.encode('hello world'); // [15339, 1917]
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  ```
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+
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+ ### Transformers/Tokenizers
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+ ```py
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+ from transformers import GPT2TokenizerFast
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+
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+ tokenizer = GPT2TokenizerFast.from_pretrained('Xenova/text-embedding-ada-002')
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+ assert tokenizer.encode('hello world') == [15339, 1917]
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+ ```