---
title: distilbert-base-cased-distilled-squad
canonical_url: "https://www.modelscope.cn/models/AI-ModelScope/distilbert-base-cased-distilled-squad"
md_url: "https://www.modelscope.cn/models/AI-ModelScope/distilbert-base-cased-distilled-squad.md"
repository: AI-ModelScope/distilbert-base-cased-distilled-squad
last_updated: 2025-07-11
model_type:
  - distilbert
architectures:
  - DistilBertForQuestionAnswering
base_model:
  - distilbert-base-cased-distilled-squad
base_model_relation: finetune
library_name:
  - onnx
  - pytorch
frameworks:
  - pytorch
downloads: 2529
stars: 0
---

# distilbert-base-cased-distilled-squad

> distilbert-base-cased-distilled-squad - AI-ModelScope 在 ModelScope 开源的模型。https://huggingface.co/distilbert-base-cased-distilled-squad with ONNX weights to be compatible with Transformers.js.

AI-ModelScope/distilbert-base-cased-distilled-squad 是 ModelScope 魔搭社区上的机器学习模型，基于 distilbert-base-cased-distilled-squad 构建。

- **Repository**: AI-ModelScope/distilbert-base-cased-distilled-squad
- **Base model**: distilbert-base-cased-distilled-squad
- **Downloads**: 2529
- **Stars**: 0
- **Last updated**: 2025-07-11

Source: https://www.modelscope.cn/models/AI-ModelScope/distilbert-base-cased-distilled-squad

---

https://huggingface.co/distilbert-base-cased-distilled-squad 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:** Run question answering.

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

const answerer = await pipeline('question-answering', 'Xenova/distilbert-base-cased-distilled-squad');
const question = 'Who was Jim Henson?';
const context = 'Jim Henson was a nice puppet.';
const output = await answerer(question, context);
```

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`).