roberta-base + Task Transfer (NER) --> Domain-Specific QA
Objective:
This is Roberta Base without any Domain Adaptive Pretraining --> 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/roberta-base-MITmovie was used as the Roberta Base + NER model.
Language model: roberta-base Language: English Downstream-task: NER --> QA Training data: MIT Movie, SQuADv1 Eval data: MoviesQA (From https://github.com/ibm-aur-nlp/domain-specific-QA) Infrastructure: 4x Tesla v100 Code: See example
Hyperparameters
Num examples = 88567
Num Epochs = 3
Instantaneous batch size per device = 32
Total train batch size (w. parallel, distributed & accumulation) = 128
