I will help you with nlp sentence classification

Sentence classification using pre-trained language models and transfer learning.

Reach higher accuracy with less data

Over 90% accuracy for two classes and 100 examples.

More examples are better (> 50 per class)

 Faster training

For people not versed in the topic, since were talking about classifiers, then we are in the supervised-learning domain of machine learning. Which would mean we need a labeled dataset to train such a model. 

The labeled dataset could be a list of messages and labeled (spam or not spam for each message).

Or sentiment analysis of movie, music, product- reviews labeled to positive/negative/neutral or to categories like ambiance, deals, service, security, comfort.

Or categorization of technical documents, specifications classified to processor documentation, computer, wifi router or furniture, lighting, gardening, bathroom, outdoor. The working model can be set up in Google Colab or as Streamlit app that reads in text or documents and return classifications or executed locally with a jupyter notebook.