I am developping a QG (Question Generation) application. Where my data is on the following format:
[{"context":"Some Context",
"question":["question1", "question2", "question3"]},
{"context":"Some other  Context",
"question":["question1"]},
{"context":" Another Context",
"question":["question1", "question2", "question3","question4"]},

...
]

In a fist moment I have created dataframe replicating the context for every question. So I have created this Dataset that works well, but dont allows me to correctly track my metrics (BLEU,ROUGE,…).
from typing import List, Union,Optional
class CustomDataset(Dataset):
    def __init__(self,PREFIX,tokenizer,X_context:np.ndarray,y_question:Optional[np.ndarray]=[],
                 source_max_length: int = 32, target_max_length: int = 32):
        
        self.tokenizer = tokenizer
        self.X_context = X_context
        
        self.y_question = y_question
        self.source_max_length = source_max_length + len(PREFIX.split(' '))
        self.target_max_length = target_max_length
        self.PREFIX = PREFIX
        

    def __len__(self):
        return len(self.X_context)
    
    def __getitem__(self, idx):
        #Source
        original_source = self.X_context[idx]
        source = f"{PREFIX} {original_source}"
        source_encoder = self.encoder_plus(source,self.source_max_length)
        source_token_ids = source_encoder['input_ids']
        source_mask = source_encoder['attention_mask']
        source_token_ids = torch.tensor(source_token_ids).type(torch.long)
        source_mask = torch.tensor(source_mask).type(torch.long)
        
        # Target
        original_target = self.y_question[idx]
        target = f"{original_target}"
        target_encoder = self.encoder_plus(target,self.target_max_length)
        target_token_ids = target_encoder['input_ids']
        target_mask = target_encoder['attention_mask']
        target_token_ids = torch.tensor(target_token_ids).type(torch.long)
        target_mask = torch.tensor(target_mask).type(torch.long)

        return (source_token_ids, source_mask, target_token_ids, target_mask,
                original_source, original_target)
          
    
    def encoder_plus(self,text,L):
      return self.tokenizer.encode_plus(text,
                                        max_length = L,
                                        truncation=True,
                                        padding="max_length")

But now I  want to have all the questions that are correlated with the same context on the same batch to be able to track all metrics on my model. The problem is when I generate a question, I they dont are on the same batch I cant know to wich question compare. I image that possibily i could use a collate function, but cant figure out how.