---
datasets:
- MIT Movie (NER Dataset)
- SQuAD

language: 
- English

thumbnail: 

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](https://github.com/adityaarunsinghal/Domain-Adaptation/blob/master/scripts/shell_scripts/movieR_NER_squad.sh)    

## 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](https://github.com/adityaarunsinghal/Domain-Adaptation/)

---