# 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/)

---

{"language": ["English"], "license": "cc-by-4.0", "tags": ["roberta", "roberta-base", "question-answering", "qa", "movies"], "datasets": ["imdb", "cornell_movie_dialogue", "SQuAD"]}
transformers pytorch tf jax roberta question-answering roberta-base qa movies dataset:imdb dataset:cornell_movie_dialogue dataset:SQuAD license:cc-by-4.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tf #jax #roberta #question-answering #roberta-base #qa #movies #dataset-imdb #dataset-cornell_movie_dialogue #dataset-SQuAD #license-cc-by-4.0 #endpoints_compatible #region-us 

# 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.  
  URL was used as the MovieRoberta.
  


## 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 URL  
Infrastructure: 1x Tesla v100   
Code:  See example    

## Hyperparameters
 
## 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

---

# 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.  
  URL was used as the MovieRoberta. ## 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 URL  
Infrastructure: 1x Tesla v100   
Code:  See example ## Hyperparameters ## 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

---
TAGS
#transformers #pytorch #tf #jax #roberta #question-answering #roberta-base #qa #movies #dataset-imdb #dataset-cornell_movie_dialogue #dataset-SQuAD #license-cc-by-4.0 #endpoints_compatible #region-us 
 # 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.  
  URL was used as the MovieRoberta. ## 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 URL  
Infrastructure: 1x Tesla v100   
Code:  See example ## Hyperparameters ## 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

---
passage: TAGS
#transformers #pytorch #tf #jax #roberta #question-answering #roberta-base #qa #movies #dataset-imdb #dataset-cornell_movie_dialogue #dataset-SQuAD #license-cc-by-4.0 #endpoints_compatible #region-us 
# 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.  
  URL was used as the MovieRoberta.## 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 URL  
Infrastructure: 1x Tesla v100   
Code:  See example## Hyperparameters## 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

---