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---
datasets:
- imdb
- cornell_movie_dialogue
- SQuAD
language:
- English
thumbnail:
tags:
- roberta
- roberta-base
- question-answering
- qa
- movies
license: cc-by-4.0
---
# roberta-base + DAPT + Domain-Specific QA
Objective:
This is Roberta Base with Domain Adaptive Pretraining on Movie Corpora --> Then a changed head to do the SQuAD Task. This makes a QA model capable of answering questions in the movie domain.
https://huggingface.co/thatdramebaazguy/movie-roberta-base was used as the MovieRoberta.
```
model_name = "thatdramebaazguy/movie-roberta-squad"
pipeline(model=model_name, tokenizer=model_name, revision="v1.0", task="question-answering")
```
## Overview
**Language model:** roberta-base
**Language:** English
**Downstream-task:** QA
**Training data:** imdb, polarity movie data, cornell_movie_dialogue, 25mlens movie names, SQuADv1
**Eval data:** MoviesQA (From https://github.com/ibm-aur-nlp/domain-specific-QA)
**Infrastructure**: 1x Tesla v100
**Code:** See [example](https://github.com/adityaarunsinghal/Domain-Adaptation/blob/master/scripts/shell_scripts/train_movieR_just_squadv1.sh)
## Hyperparameters
```
Num examples = 88567
Num Epochs = 10
Instantaneous batch size per device = 32
Total train batch size (w. parallel, distributed & accumulation) = 32
```
## Performance
### Eval on MoviesQA
- eval_samples = 5032
- exact_match = 51.64944
- f1 = 65.53983
### Eval on SQuADv1
- exact_match = 81.23936
- f1 = 89.27827
Github Repo:
- [Domain-Adaptation Project](https://github.com/adityaarunsinghal/Domain-Adaptation/)
---
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