Create README.md

README.md

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+fine-tuned bert-base-chinese for intent recognition task on [dataset](https://huggingface.co/datasets/nlp-guild/intent-recognition-biomedical)
+
+# Usage
+```python
+
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from transformers import TextClassificationPipeline
+
+tokenizer = AutoTokenizer.from_pretrained("nlp-guild/bert-base-chinese-finetuned-intent_recognition-biomedical")
+model = AutoModelForSequenceClassification.from_pretrained("nlp-guild/bert-base-chinese-finetuned-intent_recognition-biomedical")
+nlp = TextClassificationPipeline(model = model, tokenizer = tokenizer)
+
+label_set = [
+'定义',
+'病因',
+'预防',
+'临床表现(病症表现)',
+'相关病症',
+'治疗方法',
+'所属科室',
+'传染性',
+'治愈率',
+'禁忌',
+'化验/体检方案',
+'治疗时间',
+'其他'
+]
+
+def readable_results(top_k:int, usr_query:str):
+    raw = nlp(usr_query, top_k = top_k)
+    def f(x):
+        index = int(x['label'][6:])
+        x['label'] = label_set[index]
+    
+    for i in raw:
+        f(i)
+    return raw
+ 
+ readable_results(3,'得了心脏病怎么办')
+
+'''
+[{'label': '治疗方法', 'score': 0.9994503855705261},
+ {'label': '其他', 'score': 0.00018375989748165011},
+ {'label': '临床表现(病症表现)', 'score': 0.00010841596667887643}]
+'''
+```