ValueError: You have to specify either decoder_inputs or decoder_inputs_embeds
Author: puraminy
Pull request: no

Why there is no training example for T5 or MT5??? 
Could you please give me a link to an example? I had a hard time to write a code with various errors:
This is my code:
```
import torch
from datasets import load_dataset
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
#from transformers import MT5Model, T5Tokenizer
from transformers import MT5ForConditionalGeneration, T5Tokenizer

#tokenizer = AutoTokenizer.from_pretrained("google/mt5-base")
tokenizer = T5Tokenizer.from_pretrained("google/mt5-small")
raw_datasets = load_dataset("atomic")

def tokenize_function(examples):
    return tokenizer(examples["event"],max_length=128, padding="max_length", truncation=True)

def tokenize_labels(examples):
    with tokenizer.as_target_tokenizer():
         return tokenizer(examples["oReact"], return_tensors="pt")

tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)
#labels = raw_datasets.map(tokenize_labels, batched=True)
small_train_dataset = tokenized_datasets["train"].shuffle(seed=42).select(range(1000))
small_eval_dataset = tokenized_datasets["test"].shuffle(seed=42).select(range(1000))
full_train_dataset = tokenized_datasets["train"]
full_eval_dataset = tokenized_datasets["test"]


from transformers import TrainingArguments

training_args = TrainingArguments("test_trainer")
#model = AutoModelForSeq2SeqLM.from_pretrained("google/mt5-base")
model = MT5ForConditionalGeneration.from_pretrained("google/mt5-small", torchscript = True)
#traced_model = torch.jit.trace(model, (input_ids, attention_mask, decoder_input_ids))

from transformers import Trainer

trainer = Trainer(
   model=model, args=training_args, train_dataset=small_train_dataset, eval_dataset=small_eval_dataset
)

trainer.train()
import numpy as np
from datasets import load_metric

metric = load_metric("accuracy")

def compute_metrics(eval_pred):
    logits, labels = eval_pred
    predictions = np.argmax(logits, axis=-1)
    return metric.compute(predictions=predictions, references=labels)

trainer = Trainer(
    model=model,
   args=training_args,
    train_dataset=small_train_dataset,
    eval_dataset=small_eval_dataset,
    compute_metrics=compute_metrics,
)
trainer.evaluate()

```
I don't know how to feed the labels to this model...

And this is error:
```
...
 tr_loss += self.training_step(model, inputs)
  File "/home/pouramini/miniconda3/lib/python3.7/site-packages/transformers/trainer.py", line 1250, in training_step
    loss = self.compute_loss(model, inputs)
  File "/home/pouramini/miniconda3/lib/python3.7/site-packages/transformers/trainer.py", line 1277, in compute_loss
    outputs = model(**inputs)
  File "/home/pouramini/miniconda3/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/home/pouramini/miniconda3/lib/python3.7/site-packages/transformers/models/t5/modeling_t5.py", line 1510, in forward
    return_dict=return_dict,
  File "/home/pouramini/miniconda3/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/home/pouramini/miniconda3/lib/python3.7/site-packages/transformers/models/t5/modeling_t5.py", line 871, in forward
    raise ValueError(f"You have to specify either {err_msg_prefix}inputs or {err_msg_prefix}inputs_embeds")
```

@sgugger @patrickvonplaten @patil-suraj 

Comments:
Hi there, 

the [summarization](https://github.com/huggingface/transformers/tree/master/examples/pytorch/summarization) and [translation](https://github.com/huggingface/transformers/tree/master/examples/pytorch/translation) examples supports fine-tuning T5 and mT5 (and other seq2seq models in the lib). Please take a look at the readme and the script.

The scripts are easily modifiable to support training on any seq2seq task.

Also there are multiple notebook on T5 training in [community notebooks ](https://huggingface.co/transformers/community.html#community-notebooks)section. Hope that helps.

Thank you very much! I haven't found the first examples which are up to date too.

I found community notebooks later, some of them are old. Maybe a recent one in the main notebooks is a good idea.

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