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roberta-base + DAPT + Task Transfer for Domain-Specific QA

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  


Eval on SQuADv1

  • eval_samples = 10790
  • exact_match = 83.0274
  • f1 = 90.1615

Eval on MoviesQA

  • eval_samples = 5032
  • exact_match = 51.64944
  • f1 = 65.53983

Github Repo:

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