I trained a BERT based encoder decoder model: ed_model
I tokenized the input with:
txt = "I love huggingface"
inputs = input_tokenizer(txt, return_tensors="pt").to(device)
print(inputs)

The output clearly shows that a input_ids is the return dict

{'input_ids': tensor([[ 101, 5660, 7975, 2127, 2053, 2936, 5061,  102]], device='cuda:0'), 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0]], device='cuda:0'), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1]], device='cuda:0')}

But when I try to predict, I get this error:
ed_model.forward(**inputs)
ValueError: You have to specify either input_ids or inputs_embeds
Any ideas ?