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README.md
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---
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language: en
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license: apache-2.0
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library_name: transformers
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tags:
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- pharmacy
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- drug-classification
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- onnx
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---
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# 药品分类模型（ONNX格式）
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## 模型描述
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BERT-base微调的药品分类模型，转换为ONNX格式
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## 使用方式
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```python
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from transformers import AutoTokenizer, pipeline
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from onnxruntime import InferenceSession
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tokenizer = AutoTokenizer.from_pretrained("您的用户名/模型名")
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session = InferenceSession("model.onnx")
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# 预处理
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inputs = tokenizer("I have a headache", return_tensors="np")
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# 推理
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outputs = session.run(None, dict(inputs))
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predicted_id = outputs[0].argmax()
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---
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language: en
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license: apache-2.0
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library_name: transformers
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tags:
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- pharmacy
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- drug-classification
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- onnx
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---
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# 药品分类模型（ONNX格式）
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## 模型描述
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BERT-base微调的药品分类模型，转换为ONNX格式
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## 使用方式
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```python
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from transformers import AutoTokenizer, pipeline
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from onnxruntime import InferenceSession
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tokenizer = AutoTokenizer.from_pretrained("您的用户名/模型名")
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session = InferenceSession("model.onnx")
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# 预处理
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inputs = tokenizer("I have a headache", return_tensors="np")
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# 推理
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outputs = session.run(None, dict(inputs))
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predicted_id = outputs[0].argmax()
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##前端使用方式
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<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8" />
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<meta name="viewport" content="width=device-width, initial-scale=1.0" />
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<title>Transformers.js Example</title>
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</head>
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<body>
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<h1>Transformers.js in Browser</h1>
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<script type="module">
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import { pipeline } from 'https://cdn.jsdelivr.net/npm/@xenova/transformers';
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// 创建一个情感分析的 pipeline
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const classifier = await pipeline('text-classification','xuxiaoda/drug_classification');
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// 执行情感分析
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const result = await classifier('What are the drugs for treating high blood pressure?');
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console.log(result[0].label);
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</script>
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</body>
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</html>
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