Hello,
I’m working on using T5LM (small) for semantic parsing tasks. However, there seems to be discrepancies between the model properties as listed on the T5 documentation page: https://huggingface.co/docs/transformers/model_doc/t5
versus the error outputs I get from trying to run the Supervised Training example. More specifically, when running the last line:
 loss = model(input_ids=input_ids, labels=labels).loss 
I get the error saying the model needs either  decoder_inputs_embeds  or  decoder_input_ids  in place of the labels parameter. Additionally, the tutorial text states:

The model will automatically create the decoder_input_ids based on the labels ,…

Is there source code for this model implementation that I can reference? I don’t understand the difference between the embeds vs decoder input ids for a start. And I can’t tell if the model I imported using
 model = transformers.AutoModel.from_pretrained(“google/t5-small-lm-adapt”) 
is correct because in addition to the above discrepancy, when I run the original loss command, I got the error that
 Seq2SeqModelOutput  does not have a  .loss  function.
I thought the LM adaptation of T5 would be 1-to-1 to the original T5 architecture, but that clearly doesn’t seem to be the case. Any help or direction would be greatly appreciated.
Thanks,
Selma