metadata

datasets:
  - MIT Movie (NER Dataset)
  - SQuAD
language:
  - English
thumbnail: null
tags:
  - roberta
  - roberta-base
  - question-answering
  - qa
  - movies
license: cc-by-4.0

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.

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

Overview

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

Performance

Eval on MoviesQA

eval_samples = 5032
exact_match = 58.0684
f1 = 71.3717

Github Repo:

Domain-Adaptation Project