unable to completely load T5 pretrained model; missing/unexpected keys
# 🐛 Bug

## Information

Model I am using: T5

## To reproduce

```
model, info = T5ForConditionalGeneration.from_pretrained('t5-small',output_loading_info=True)
```
info is 
`
{'missing_keys': ['encoder.embed_tokens.weight', 'decoder.embed_tokens.weight', 'lm_head.weight'], 'unexpected_keys': ['encoder.block.0.layer.0.layer_norm.bias', 'encoder.block.0.layer.1.layer_norm.bias', 'encoder.block.1.layer.0.layer_norm.bias', 'encoder.block.1.layer.1.layer_norm.bias', 'encoder.block.2.layer.0.layer_norm.bias', 'encoder.block.2.layer.1.layer_norm.bias', 'encoder.block.3.layer.0.layer_norm.bias', 'encoder.block.3.layer.1.layer_norm.bias', 'encoder.block.4.layer.0.layer_norm.bias', 'encoder.block.4.layer.1.layer_norm.bias', 'encoder.block.5.layer.0.layer_norm.bias', 'encoder.block.5.layer.1.layer_norm.bias', 'encoder.final_layer_norm.bias', 'decoder.block.0.layer.0.layer_norm.bias', 'decoder.block.0.layer.1.layer_norm.bias', 'decoder.block.0.layer.2.layer_norm.bias', 'decoder.block.1.layer.0.layer_norm.bias', 'decoder.block.1.layer.1.layer_norm.bias', 'decoder.block.1.layer.2.layer_norm.bias', 'decoder.block.2.layer.0.layer_norm.bias', 'decoder.block.2.layer.1.layer_norm.bias', 'decoder.block.2.layer.2.layer_norm.bias', 'decoder.block.3.layer.0.layer_norm.bias', 'decoder.block.3.layer.1.layer_norm.bias', 'decoder.block.3.layer.2.layer_norm.bias', 'decoder.block.4.layer.0.layer_norm.bias', 'decoder.block.4.layer.1.layer_norm.bias', 'decoder.block.4.layer.2.layer_norm.bias', 'decoder.block.5.layer.0.layer_norm.bias', 'decoder.block.5.layer.1.layer_norm.bias', 'decoder.block.5.layer.2.layer_norm.bias', 'decoder.final_layer_norm.bias'], 'error_msgs': []}
`
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## Expected behavior

No keys should be missing or unexpected
<!-- A clear and concise description of what you would expect to happen. -->

## Environment info
<!-- You can run the command `python transformers-cli env` and copy-and-paste its output below.
     Don't forget to fill out the missing fields in that output! -->
     
- `transformers` version: 2.7.0
- Platform: Ubuntu
- Python version: 3.6
- PyTorch version (GPU?): 1.2.0 (yes)
- Tensorflow version (GPU?): nope
- Using GPU in script?: yes
- Using distributed or parallel set-up in script?: nope

Hi @dhecloud, 

Thanks for you issue :-) 
Does the model still work fine? <|||||>> Hi @dhecloud,
> 
> Thanks for you issue :-)
> Does the model still work fine?

Hi, thanks for your reply.
Using the examples provided in the doc, the model works fine. 
Before i used `T5WithLMHeadModel` in version `2.5.1` which did not raise this missing keys warning.  After i moved to `T5ForConditionalGeneration` in `2.7.0` there was this warning and my training loss diverged so i thought i might raise this issue in case there was some sort of change in naming in the checkpoint<|||||>I'm gonna take a look :-) <|||||>Hi guys, 
Any news on this?
When I try to load t5-base I receive this:


INFO:transformers.modeling_utils:Weights of T5ForConditionalGeneration not initialized from pretrained model: ['encoder.embed_tokens.weight', 'decoder.embed_tokens.weight', 'lm_head.weight']<|||||>> Hi guys,
> Any news on this?
> When I try to load t5-base I receive this:
> 
> INFO:transformers.modeling_utils:Weights of T5ForConditionalGeneration not initialized from pretrained model: ['encoder.embed_tokens.weight', 'decoder.embed_tokens.weight', 'lm_head.weight']

i think it's mostly a harmless misplaced error. The model should still work fine. You can test it by trying out the examples <|||||>Yeah this should not be a problem, all these weights are `[encoder.embed_tokens.weight', 'decoder.embed_tokens.weight', 'lm_head.weight']` are weights tied to the input embedding matrix and therefore don't need to be initialized.<|||||>How can we silence the error?<|||||>It should be enough to lower the cli-logger