Odd predictions of T5 models in recent versions 
We are seeing odd predictions by T5 models ([UnifiedQA models](https://github.com/allenai/unifiedqa/)) when using the recent HF version (4.2.1).  Here is the discussion: https://github.com/allenai/unifiedqa/issues/11 

### Who can help
@TevenLeScao @patrickvonplaten

## To reproduce
Try running the following script: 
```python 
from transformers import AutoTokenizer, T5ForConditionalGeneration

model_name = "allenai/unifiedqa-t5-small" # you can specify the model size here
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = T5ForConditionalGeneration.from_pretrained(model_name)

def run_model(input_string, **generator_args):
    input_ids = tokenizer.encode(input_string, return_tensors="pt")
    res = model.generate(input_ids, **generator_args)
    return [tokenizer.decode(x) for x in res]

run_model("which is best conductor? \\n (a) iron (b) feather")
```

 - For `transformers==4.2.1`,  I am getting `['<pad> iron</s>']`, which is not good. 
 - However, `transformers==3.5.1`and `transformers==3.1.0` give me `['iron']`which is the expected response. 


Hello @danyaljj,

This is expected actually. Could you change your code as follows to get the previous results:

```python
from transformers import AutoTokenizer, T5ForConditionalGeneration

model_name = "allenai/unifiedqa-t5-small" # you can specify the model size here
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = T5ForConditionalGeneration.from_pretrained(model_name)

def run_model(input_string, **generator_args):
    input_ids = tokenizer.encode(input_string, return_tensors="pt")
    res = model.generate(input_ids, **generator_args)
    return tokenizer.batch_decode(res, skip_special_tokens=True)

run_model("which is best conductor? \\n (a) iron (b) feather")
```<|||||>Thanks for the quick reply! The new code works! Thanks! 