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README.md

@@ -12,20 +12,12 @@ pipeline_tag: text-classification
 
 # Qwen 3B Medical Department Classifier (400 Training Samples)
 
-This is a fine-tuned Qwen 3B model for medical department classification on Chinese medical dialogues, trained with 400 samples.
+This is a fine-tuned Qwen 3B model for medical department classification on Chinese medical dialogues.
 
-## Model Details
-
-- **Base Model**: Qwen 3B
-- **Task**: Medical Department Classification
-- **Language**: Chinese (zh)
-- **Training Size**: 400 samples
-- **Number of Classes**: 44
-- **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']
 
 ## Model Description
 
-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 using 400 training samples.
+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.
 
 ## Usage
 
@@ -36,16 +28,11 @@ from transformers import AutoModelForSequenceClassification, AutoTokenizer
 model = AutoModelForSequenceClassification.from_pretrained("Xiaolihai/qwen-3b-medical-classifier-400")
 tokenizer = AutoTokenizer.from_pretrained("Xiaolihai/qwen-3b-medical-classifier-400")
 
-# Example usage
-text = "患者描述胸痛症状，需要进一步检查"
-inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
-outputs = model(**inputs)
-predictions = outputs.logits.argmax(dim=-1)
 ```
 
 ## Training
 
-The model was fine-tuned on medical dialogue data with 400 training samples.
+The model was fine-tuned on medical dialogue data.
 
 ## License