# 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/)

---

{"language": ["English"], "license": "cc-by-4.0", "tags": ["roberta", "roberta-base", "question-answering", "qa", "movies"], "datasets": ["MIT Movie", "SQuAD"]}
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 + 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).  
  URL was used as the Roberta Base + NER model.
  


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

## Hyperparameters
 
## 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

---

# 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).  
  URL was used as the Roberta Base + NER model. ## Overview
Language model: roberta-base  
Language: English  
Downstream-task: NER --> QA  
Training data: MIT Movie, SQuADv1  
Eval data: MoviesQA (From URL  
Infrastructure: 4x Tesla v100   
Code:  See example ## Hyperparameters ## 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

---
TAGS
#transformers #pytorch #tf #jax #roberta #question-answering #roberta-base #qa #movies #license-cc-by-4.0 #endpoints_compatible #region-us 
 # 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).  
  URL was used as the Roberta Base + NER model. ## Overview
Language model: roberta-base  
Language: English  
Downstream-task: NER --> QA  
Training data: MIT Movie, SQuADv1  
Eval data: MoviesQA (From URL  
Infrastructure: 4x Tesla v100   
Code:  See example ## Hyperparameters ## 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

---
passage: TAGS
#transformers #pytorch #tf #jax #roberta #question-answering #roberta-base #qa #movies #license-cc-by-4.0 #endpoints_compatible #region-us 
# 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).  
  URL was used as the Roberta Base + NER model.## Overview
Language model: roberta-base  
Language: English  
Downstream-task: NER --> QA  
Training data: MIT Movie, SQuADv1  
Eval data: MoviesQA (From URL  
Infrastructure: 4x Tesla v100   
Code:  See example## Hyperparameters## 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

---