---
title: text-embedding-ada-002
canonical_url: "https://www.modelscope.cn/models/Xenova/text-embedding-ada-002"
md_url: "https://www.modelscope.cn/models/Xenova/text-embedding-ada-002.md"
repository: Xenova/text-embedding-ada-002
last_updated: 2025-08-20
library_name:
  - transformer
  - pytorch
frameworks:
  - pytorch
downloads: 3435
stars: 21
tags:
  - transformers.js
  - tokenizers
---

# text-embedding-ada-002

> text-embedding-ada-002 - Xenova 在 ModelScope 开源的模型。text-embedding-ada-002 Tokenizer

- **Repository**: Xenova/text-embedding-ada-002
- **Tags**: transformers.js, tokenizers
- **Downloads**: 3435
- **Stars**: 21
- **Last updated**: 2025-08-20

Source: https://www.modelscope.cn/models/Xenova/text-embedding-ada-002

---

# text-embedding-ada-002 Tokenizer

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).

## Usage

### Transformers/Tokenizers
```py
from transformers import GPT2TokenizerFast

tokenizer = GPT2TokenizerFast.from_pretrained('Xenova/text-embedding-ada-002')
assert tokenizer.encode('hello world') == [15339, 1917]
```

### 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
```

```js
import { AutoTokenizer } from '@huggingface/transformers';

const tokenizer = await AutoTokenizer.from_pretrained('Xenova/text-embedding-ada-002');
const tokens = tokenizer.encode('hello world'); // [15339, 1917]
```