The code below is taken from the tutorial 4
from datasets import load_metric

metric= load_metric("glue", "mrpc")
model.eval()
for batch in eval_dataloader:
    batch = {k: v.to(device) for k, v in batch.items()}
    with torch.no_grad():
        outputs = model(**batch)
    
    logits = outputs.logits
    predictions = torch.argmax(logits, dim=-1)
    metric.add_batch(predictions=predictions, references=batch["labels"])

metric.compute()

Inside the loop for batch in eval_dataloader:, how can I know which indices from the dataset this batch includes?
The DataLoader is created earlier using
eval_dataloader = DataLoader(
    tokenized_datasets["validation"], batch_size=8, collate_fn=data_collator
)

Note that it’s without the shuffling flag, so it’s possible to count manually using batch size, but how to do it with shuffling? Is it possible to to make it a field of the batch when creating the dataset and dataloader?