How to deal with DataCollator and DataLoaders in Huggingface?
I have issues combining a DataLoader and DataCollator. The following code with DataCollatorWithPadding results in aValueError: Unable to create tensor, you should probably activate truncation and/or padding with 'padding=True' 'truncation=True' to have batched tensors with the same length.when I want to iterate through the batches.from torch.utils.data.dataloader import DataLoader
from transformers import DataCollatorWithPadding
data_collator = DataCollatorWithPadding(tokenizer)
train_dataloader = DataLoader(train_dataset, shuffle=True, batch_size=16, 
collate_fn=data_collator)
eval_dataloader = DataLoader(eval_dataset, batch_size=16, collate_fn=data_collator)
for epoch in range(2):
    model.train()
    for step, batch in enumerate(train_dataloader):          
          outputs = model(**batch)
          loss = outputs.lossHowever, I found annother approach where I changed the DataCollator tolambda x: xThen it gives me aTypeError: DistilBertForSequenceClassification object argument after ** must be a mapping, not listfrom torch.utils.data.dataloader import DataLoader
train_dataloader = DataLoader(train_dataset, shuffle=True, batch_size=16, collate_fn=lambda x: x  )
eval_dataloader = DataLoader(eval_dataset, batch_size=16, collate_fn=lambda x: x)
for epoch in range(2):
    model.train()
    for step, batch in enumerate(train_dataloader):          
          outputs = model(**batch)
          loss = outputs.lossFor reproducability and for the rest of the code I provide you a Jupyter Notebook on Google Colab. You find the errors at the bottom of the notebook. Using the trainer class in my particular scenario is no option.Colab Notebook