readme: ## Multiple Prediction Heads
  * ExtractiveQA Head 
  * Three Class Classification Head, classes => (yes, no, extra_qa) to answer binary questions or direct to ExtractiveQA Head
  
  ## BoolQ Validation dataset Evaluation: <br/>
  support => 3270 <br/>
  accuracy => 0.73 <br/>
  macro f1 => 0.71
  
  ## SQuAD Validation dataset Evaluation: <br/>
  eval_HasAns_exact      = 78.0196 <br/>
  eval_HasAns_f1         = 84.0327 <br/>
  eval_HasAns_total      =    5928 <br/>
  eval_NoAns_exact       = 81.8167 <br/>
  eval_NoAns_f1          = 81.8167 <br/>
  eval_NoAns_total       =    5945 <br/>
  eval_best_exact        = 79.9208 <br/>
  eval_best_f1           = 82.9231 <br/>
  eval_exact             = 79.9208 <br/>
  eval_f1                = 82.9231 <br/>
  eval_samples           =   12165 <br/>
  eval_total             =   11873 
  
  ## Uasge in transformers
  Import the script from [here](https://huggingface.co/shahrukhx01/roberta-base-squad2-boolq-baseline/blob/main/multitask_model.py)
  ```python
  from multitask_model import RobertaForMultitaskQA
  from transformers import RobertaTokenizerFast
  device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
  model = RobertaForMultitaskQA.from_pretrained(
          "shahrukhx01/roberta-base-squad2-boolq-baseline",
          task_labels_map={"squad_v2": 2, "boolq": 3},
      ).to(device)
  tokenizer = RobertaTokenizerFast.from_pretrained("shahrukhx01/roberta-base-squad2-boolq-baseline")
  ```