Idea: My model takes in documents from the user using Standard Input - Then the model classifies, explains why it classified the way it did, and asks the user i

Idea: My model takes in documents from the user using Standard Input - Then the model classifies, explains why it classified the way it did, and asks the user if the predicted label is the ground truth or not. User supplies the ground truth, the model incrementally trains on the new example, and that new example (with human supplied label) is appended to my dataset and the cycle continues. This is called active learning I want to take in massive sums of articles from a news API which will be placed in their corresponding file based on where my classifier says I should put them. I have to generate my own labeled data for this. That is a problem. Most people don't realize that the sample efficiency in models which utilize transfer learning is so great that AI-assisted data labeling is extremely useful and can significantly shorten what is ordinarily a painful data labeling process. We need a way to quickly create word embedding powered document classifiers which learn with a human in the loop. For some classes, an extremely limited number of examples may be all that is necessary to get results that a user would consider to be successful for their task. I want to know what my model is learning - so I integrate the word embeddings available with Flair, combine with Classifiers in Sklearn and Tensorflow/Keras/PyTorch, and finish it off with a nice squeeze of the LIME algorithm for model interpretability (implemented within the ELI5 Library)