Objective: This is Roberta Base with Domain Adaptive Pretraining on Movie Corpora --> Then trained for the NER task using MIT Movie Dataset --> Then a changed head to do the SQuAD Task. This makes a QA model capable of answering questions in the movie domain, with additional information coming from a different task (NER - Task Transfer).
https://huggingface.co/thatdramebaazguy/movie-roberta-base was used as the MovieRoberta.

model_name = "thatdramebaazguy/movie-roberta-MITmovie-squad"
pipeline(model=model_name, tokenizer=model_name, revision="v1.0", task="question-answering")

Language model: roberta-base
Language: English
Downstream-task: NER --> QA
Training data: imdb, polarity movie data, cornell_movie_dialogue, 25mlens movie names, MIT Movie, SQuADv1
Eval data: MoviesQA (From https://github.com/ibm-aur-nlp/domain-specific-QA)
Infrastructure: 4x Tesla v100
Code: See example

Num examples = 88567  
Num Epochs = 3  
Instantaneous batch size per device = 32  
Total train batch size (w. parallel, distributed & accumulation) = 128  

Github Repo: