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---
datasets:
- MIT Movie
- 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 = 55.80286
- f1 = 70.31451
### Eval on SQuADv1
- exact_match = 85.6859
- f1 = 91.96064
Github Repo:
- [Domain-Adaptation Project](https://github.com/adityaarunsinghal/Domain-Adaptation/)
---
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