Classifier of human behavioral activities

Based on the ernie3.0 framework, the ernie3.0 pre-training model and tokenizer are loaded through PaddleNLP, the text is divided into non-activities and activities, and then the non-activities and activities are divided into non-necessary activities and necessary activities respectively; optional activities and social activities, and then define the optimizer, loss function, evaluation index, etc. required for training, and pre-model fine-tuning, through pre-training, let the machine learn to automatically analyze these semantic tendencies. Secondly, Python is used to crawl the Weibo tweet data, and the data is cleaned and preprocessed to provide basic data for classification research. Finally, based on Ernie 3.0 training and learning, the microblog text is predicted, and it is divided into spontaneous activities, social activities and other activities according to the recognition results, and the prediction results are saved.本数据集基于ERNIE 3.0框架，通过PaddleNLP加载ERNIE 3.0预训练模型与分词器（tokenizer）。首先将文本划分为非活动类与活动类，随后分别将非活动类划分为非必要活动与必要活动，将活动类划分为可选活动与社交活动；接着定义训练与预训练模型微调所需的优化器、损失函数、评价指标等组件，通过预训练使模型学习自动分析上述语义倾向。其次，通过Python爬取微博推文数据，并对数据进行清洗与预处理，为分类研究提供基础数据。最后，基于经ERNIE 3.0训练得到的模型对微博文本进行预测，根据识别结果将其划分为自发活动、社交活动与其他活动，并保存预测结果。