Text classification has numerous applications, from tweet sentiment, product reviews, toxic comments, and more. It's a popular project topic among Insight Fellows, however a lot of time is spent collecting labeled datasets, cleaning data, and deciding which classification method to use. Services like Clarifai, and Google AutoML have made it very easy to create image classification models with less labeled data, but it's not as easy to create such models for text classification. For image classification tasks, transfer learning has proven to be very effective in providing good accuracy with fewer labeled datasets. Transfer learning is a technique that enables the transfer of knowledge learned from one dataset to another.
Nov-12-2019, 09:20:45 GMT