Print All Tokens Over a Certain Probability Threshold: T5 
`This works with GPT-2, but not with T5. Is it possible to adapt this to make T5 work? This works with GPT-2, but not with T5. Is it possible to adapt this to make T5 work?`

```
import torch
import torch.nn.functional as F
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("t5-base")
model = AutoModelWithLMHead.from_pretrained("t5-base")
input_txt = "Hello, my name is Sylvain."
inputs = tokenizer(input_txt, return_tensors='pt')
outputs = model(**inputs)
predictions = F.softmax(outputs[0], dim=-1)
thresh = 1e-2
vocab_size = predictions.shape[-1]
idxs = torch.arange(0, vocab_size)[predictions[0][-1] >= thresh]
print(tokenizer.convert_ids_to_tokens(idxs))
```

`ValueError: You have to specify either decoder_inputs or decoder_inputs_embeds`
Hello, thanks for opening an issue! We try to keep the github issues for bugs/feature requests.
Could you ask your question on the [forum](https://discusss.huggingface.co) instead?

Have you checked the [T5 docs](https://huggingface.co/transformers/model_doc/t5.html) regarding the `decoder_inputs`? Are they unclear?

Thanks!<|||||>This issue has been automatically marked as stale and been closed because it has not had recent activity. Thank you for your contributions.

If you think this still needs to be addressed please comment on this thread.