As someone else alluded to, this is a task for multiple models. Fortunately, there are a lot of great NLP libraries that combine multiple pre-trained language m

As someone else alluded to, this is a task for multiple models. Fortunately, there are a lot of great NLP libraries that combine multiple pre-trained language models into a single pipeline you can interface with, like Stanza. From their docs, their vanilla pipeline breaks down the sentence "Barack Obama was born in Hawaii. He was elected president in 2008." as : ('Barack', '4', 'nsubj:pass') ('Obama', '1', 'flat') ('was', '4', 'aux:pass') ('born', '0', 'root') ('in', '6', 'case') ('Hawaii', '4', 'obl') ('.', '4', 'punct') It should be very easy to deploy Stanza's pipeline as an API endpoint. Here is an example of such a NLP-library-as-API endpoint, albeit with Hugging Face's Transformers, deployed via Cortex: https://github.com/cortexlabs/cortex/blob/master/examples/pytorch/sentiment-analyzer/predictor.py https://github.com/cortexlabs/cortex/blob/master/examples/py...