News Article Classification
Classification refers to grouping that allows easier navigation among
articles. Internet news needs to be divided into categories. This will help users to access the
news of their interest in real-time without wasting time.
Background
- With more and more media publishing a large number of news on the Internet every day, it is necessary to classify news articles so that users can effectively obtain information. The article is classified according to business, entertainment, politics, sports, science and technology and other fields. In this way, with the help of the classification system, users can find the types of articles they are interested in, which saves search time and improves reading efficiency.
Prepare Dataset
- First step: Specify the application scenario. In this example, take "Article Classification" as an example.
- Second step: Prepare different types of news content. Each line is a sample. The first column is the category of the news, and the second column is the content of the news.
- Third step: Create a dataset through LiCO.
Click [Create Dataset].
Train Model
- When your dataset preparation is complete, you can click [Create Model] to complete the model creation.
- And then click [Train Model] to start training.
Validate Model
- After the model training is completed, the model accuracy can be verified online through [Validate Model].
- You can also predict multiple lines of text through [Batch Predict].
Deploy Service
When you have a model that achieves the expected predictions, you can deploy it as sevices in LiCO.
- You can deploy the model by [Deploy Service]. In [My Services] you can manage all deployed services in current scenario. Instructions are provided for each service, which you can view it by clicking on the service id.
