Do you fine-tunde both encoder and decoder 
Hi @patil-suraj

https://github.com/huggingface/transformers/blob/277fc2cc782d8c5c28ec399b1ee2fc1c6aed7b6e/examples/pytorch/summarization/run_summarization.py#L500
I am not sure why the  `input_models` does not take  `decoder_attention_mask` into account ? 

**debugging:** 

just print the `train_dataset` after this line
https://github.com/huggingface/transformers/blob/277fc2cc782d8c5c28ec399b1ee2fc1c6aed7b6e/examples/pytorch/summarization/run_summarization.py#L518

`print(train_dataset)`  gives:

```
Dataset({
    features: ['input_ids', 'attention_mask', 'labels'],
    num_rows: 2069
}) 
```
the `attention_mask` is the attention mask of the encoder 
@rafikg Could you elaborate the question in a bit more detail? Maybe with a minimal code example that demonstrates the issue/question. In particular, could you check what are the keys in `model_inputs` at this line (when you run your example code)

https://github.com/huggingface/transformers/blob/277fc2cc782d8c5c28ec399b1ee2fc1c6aed7b6e/examples/pytorch/summarization/run_summarization.py#L501

Thank you!<|||||>@ydshieh I update the question<|||||>This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

Please note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) are likely to be ignored.