# roberta-base + DAPT + Task Transfer for Domain-Specific QA

Objective:
  This is Roberta Base with Domain Adaptive Pretraining on Movie Corpora --> 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/movie-roberta-base was used as the MovieRoberta.
  
```
model_name = "thatdramebaazguy/movie-roberta-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:** imdb, polarity movie data, cornell_movie_dialogue, 25mlens movie names, 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 SQuADv1
- eval_samples =   10790  
- exact_match  = 83.0274  
- f1           = 90.1615  

### Eval on MoviesQA
- eval_samples =    5032
- exact_match = 51.64944
- f1 = 65.53983

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", "MIT Movie"]}
transformers pytorch tf jax roberta question-answering roberta-base qa movies license:cc-by-4.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tf #jax #roberta #question-answering #roberta-base #qa #movies #license-cc-by-4.0 #endpoints_compatible #region-us 

# roberta-base + DAPT + Task Transfer for Domain-Specific QA

Objective:
  This is Roberta Base with Domain Adaptive Pretraining on Movie Corpora --> 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).  
  URL was used as the MovieRoberta.
  


## Overview
Language model: roberta-base  
Language: English  
Downstream-task: NER --> QA  
Training data: imdb, polarity movie data, cornell_movie_dialogue, 25mlens movie names, MIT Movie, SQuADv1  
Eval data: MoviesQA (From URL  
Infrastructure: 4x Tesla v100   
Code:  See example    

## Hyperparameters
 
## Performance

### Eval on SQuADv1
- eval_samples =   10790  
- exact_match  = 83.0274  
- f1           = 90.1615  

### Eval on MoviesQA
- eval_samples =    5032
- exact_match = 51.64944
- f1 = 65.53983

Github Repo: 
- Domain-Adaptation Project

---

# roberta-base + DAPT + Task Transfer for Domain-Specific QA

Objective:
  This is Roberta Base with Domain Adaptive Pretraining on Movie Corpora --> 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).  
  URL was used as the MovieRoberta. ## Overview
Language model: roberta-base  
Language: English  
Downstream-task: NER --> QA  
Training data: imdb, polarity movie data, cornell_movie_dialogue, 25mlens movie names, MIT Movie, SQuADv1  
Eval data: MoviesQA (From URL  
Infrastructure: 4x Tesla v100   
Code:  See example ## Hyperparameters ## Performance ### Eval on SQuADv1
- eval_samples =   10790  
- exact_match  = 83.0274  
- f1           = 90.1615 ### Eval on MoviesQA
- eval_samples =    5032
- exact_match = 51.64944
- f1 = 65.53983

Github Repo: 
- Domain-Adaptation Project

---
TAGS
#transformers #pytorch #tf #jax #roberta #question-answering #roberta-base #qa #movies #license-cc-by-4.0 #endpoints_compatible #region-us 
 # roberta-base + DAPT + Task Transfer for Domain-Specific QA

Objective:
  This is Roberta Base with Domain Adaptive Pretraining on Movie Corpora --> 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).  
  URL was used as the MovieRoberta. ## Overview
Language model: roberta-base  
Language: English  
Downstream-task: NER --> QA  
Training data: imdb, polarity movie data, cornell_movie_dialogue, 25mlens movie names, MIT Movie, SQuADv1  
Eval data: MoviesQA (From URL  
Infrastructure: 4x Tesla v100   
Code:  See example ## Hyperparameters ## Performance ### Eval on SQuADv1
- eval_samples =   10790  
- exact_match  = 83.0274  
- f1           = 90.1615 ### Eval on MoviesQA
- eval_samples =    5032
- exact_match = 51.64944
- f1 = 65.53983

Github Repo: 
- Domain-Adaptation Project

---
passage: TAGS
#transformers #pytorch #tf #jax #roberta #question-answering #roberta-base #qa #movies #license-cc-by-4.0 #endpoints_compatible #region-us 
# roberta-base + DAPT + Task Transfer for Domain-Specific QA

Objective:
  This is Roberta Base with Domain Adaptive Pretraining on Movie Corpora --> 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).  
  URL was used as the MovieRoberta.## Overview
Language model: roberta-base  
Language: English  
Downstream-task: NER --> QA  
Training data: imdb, polarity movie data, cornell_movie_dialogue, 25mlens movie names, MIT Movie, SQuADv1  
Eval data: MoviesQA (From URL  
Infrastructure: 4x Tesla v100   
Code:  See example## Hyperparameters## Performance### Eval on SQuADv1
- eval_samples =   10790  
- exact_match  = 83.0274  
- f1           = 90.1615### Eval on MoviesQA
- eval_samples =    5032
- exact_match = 51.64944
- f1 = 65.53983

Github Repo: 
- Domain-Adaptation Project

---