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--- |
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language: zh |
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license: mit |
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tags: |
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- medical |
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- classification |
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- chinese |
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- qwen |
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- qwen-3b |
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pipeline_tag: text-classification |
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--- |
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# Qwen 3B Medical Department Classifier |
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This is a fine-tuned Qwen 3B model for medical department classification on Chinese medical dialogues. |
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## Model Details |
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- **Base Model**: Qwen 3B |
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- **Task**: Medical Department Classification |
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- **Language**: Chinese (zh) |
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- **Number of Classes**: 44 |
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- **Classes**: ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43'] |
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## Model Description |
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This model has been fine-tuned to classify medical dialogues into appropriate medical departments. It's based on the Qwen 3B model and has been specifically trained for Chinese medical text classification. |
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## Usage |
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```python |
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from transformers import AutoModelForSequenceClassification, AutoTokenizer |
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# Load model and tokenizer |
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model = AutoModelForSequenceClassification.from_pretrained("Xiaolihai/qwen-3b-medical-classifier") |
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tokenizer = AutoTokenizer.from_pretrained("Xiaolihai/qwen-3b-medical-classifier") |
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# Example usage |
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text = "患者描述胸痛症状，需要进一步检查" |
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inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True) |
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outputs = model(**inputs) |
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predictions = outputs.logits.argmax(dim=-1) |
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``` |
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## Training |
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The model was fine-tuned on medical dialogue data with 400 training samples. |
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## License |
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This model is released under the MIT License. |
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