Hi, at training, I’m using the forward pass and batch_decode on the logits to get the decoded output:
    outputs = model(
        input_ids,
        attention_mask,
        dec_input_ids,
        dec_attention_mask,
        labels=dec_input_ids,
    )

    loss, logits = outputs.loss, outputs.logits
    decoded_output = tokenizer.batch_decode(torch.argmax(outputs.logits, dim=2).tolist(), skip_special_tokens=True)

And decoded_output seems to comply with what I trained the model on:

bread dough ; side surface

However, I’ve noticed that using model.generate produces non-sense:
    generated = model.generate(input_ids)
    tokenizer.decode(generated[0], skip_special_tokens=True))


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Note that it is the same model instance, as well as the same input_ids (this way it can’t be related to saving/loading issues, and I guess it also eliminates the possibility of encoding/tokenization issues for input_ids).
Background: model is of class T5ForConditionalGeneration and initialized with t5-small.
What’s the problem here? I’ve used the EncoderDecoderModel in the very same way, and there, model.generate works as expected.