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--- |
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base_model: naver-clova-ix/donut-base-finetuned-docvqa |
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library_name: transformers.js |
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pipeline_tag: document-question-answering |
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tags: |
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- donut |
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- image-to-text |
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- vision |
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- donut-swin |
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--- |
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https://huggingface.co/naver-clova-ix/donut-base-finetuned-docvqa with ONNX weights to be compatible with 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:** Answer questions about a document with `Xenova/donut-base-finetuned-docvqa`. |
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```js |
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import { pipeline } from '@huggingface/transformers'; |
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// Create a document question answering pipeline |
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const qa_pipeline = await pipeline('document-question-answering', 'Xenova/donut-base-finetuned-docvqa'); |
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// Generate an answer for a given image and question |
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const image = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/invoice.png'; |
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const question = 'What is the invoice number?'; |
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const output = await qa_pipeline(image, question); |
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// [{ answer: 'us-001' }] |
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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`). |
