[examples/seq2seq/README.md] fix t5 examples (#10734)

* [examples/seq2seq] fix t5 examples

This PR:
* fixes T5 examples to include `--source_prefix` - it's **not** optional. If you give it a try you will see that you get 10x worse bleu scores w/o it. w/ `27.6849`, w/ `2.374`
* added a normal translation example w/o the peculiarities of MBart and T5
* reduces the default max samples to 50 so it's much faster to test quickly

summarization seems to be broken for t5 score-wise: https://github.com/huggingface/transformers/issues/10733

@sgugger

* specify explicitly the t5 models requiring the special handling

* one more

* update the t5 summarization example to use cnn_dailymail

* move max*samples into the top level README.md

* better wording

* better wording

examples/README.md

@@ -95,6 +95,23 @@ Coming soon!
 | [**`translation`**](https://github.com/huggingface/transformers/tree/master/examples/seq2seq)                       | WMT             | ✅  | - | - | -
 
 
+## Running quick tests
+
+Most examples are equipped with a mechanism to truncate the number of dataset samples to the desired length. This is useful for debugging purposes, for example to quickly check that all stages of the programs can complete, before running the same setup on the full dataset which may take hours to complete.
+
+For example here is how to truncate all three splits to just 50 samples each:
+```
+examples/token-classification/run_ner.py \
+--max_train_samples 50 \
+--max_val_samples 50 \
+--max_test_samples 50 \
+[...]
+```
+
+Most example scripts should have the first two command line arguments and some have the third one. You can quickly check if a given example supports any of these by passing a `-h` option, e.g.:
+```
+examples/token-classification/run_ner.py -h
+```
 
 ## Resuming training