Instructions to use Xenova/bert-base-chinese-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers.js
How to use Xenova/bert-base-chinese-ner with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('token-classification', 'Xenova/bert-base-chinese-ner');
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Download README.md from Xenova/bert-base-chinese-ner: direct link, hf CLI and curl.
- Browser
- Download file 551 Bytes
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https://huggingface.co/Xenova/bert-base-chinese-ner/resolve/main/README.md
- Command line
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hf download hf://Xenova/bert-base-chinese-ner/README.md
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curl -L -o README.md https://huggingface.co/Xenova/bert-base-chinese-ner/resolve/main/README.md
551 Bytes
metadata
base_model: ckiplab/bert-base-chinese-ner
library_name: transformers.js
https://huggingface.co/ckiplab/bert-base-chinese-ner with ONNX weights to be compatible with Transformers.js.
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 and structuring your repo like this one (with ONNX weights located in a subfolder named onnx).