Bert Base Chinese Finetuned Intent Recognition Biomedical

🚀 Fine-tuned BERT-base-chinese for Intent Recognition

This project presents a fine-tuned BERT-base-chinese model specifically designed for the intent recognition task on the dataset. It aims to accurately classify intents within the biomedical domain.

🚀 Quick Start

Prerequisites

Make sure you have the transformers library installed. If not, you can install it using pip install transformers.

💻 Usage Examples

Basic Usage

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}]
'''