wrong length with datasets 
Also, I cannot first convert it to torch format, since huggingface seq2seq_trainer codes process the datasets afterwards during datacollector function to make it optimize for TPUs. 
Hi
I have a MRPC dataset which I convert it to seq2seq format, then this is of this format:

`Dataset(features: {'src_texts': Value(dtype='string', id=None), 'tgt_texts': Value(dtype='string', id=None)}, num_rows: 10)
`

I feed it to a dataloader:
```
dataloader = DataLoader(
            train_dataset,
            batch_size=self.args.train_batch_size,
            sampler=train_sampler,
            collate_fn=self.data_collator,
            drop_last=self.args.dataloader_drop_last,
            num_workers=self.args.dataloader_num_workers,
        )
```

now if I type len(dataloader) this is 1, which is wrong, and this needs to be 10. could you assist me please? thanks 

wrong length with datasets  
 Hi
I have a MRPC dataset which I convert it to seq2seq format, then this is of this format:

`Dataset(features: {'src_texts': Value(dtype='string', id=None), 'tgt_texts': Value(dtype='string', id=None)}, num_rows: 10)
`

I feed it to a dataloader:
```
dataloader = DataLoader(
            train_dataset,
            batch_size=self.args.train_batch_size,
            sampler=train_sampler,
            collate_fn=self.data_collator,
            drop_last=self.args.dataloader_drop_last,
            num_workers=self.args.dataloader_num_workers,
        )
```

now if I type len(dataloader) this is 1, which is wrong, and this needs to be 10. could you assist me please? thanks 
 
 Also, I cannot first convert it to torch format, since huggingface seq2seq_trainer codes process the datasets afterwards during datacollector function to make it optimize for TPUs. 