doc changes (#7385)

examples/README.md

@@ -24,8 +24,8 @@ This is still a work-in-progress – in particular documentation is still sparse
 | [**`question-answering`**](https://github.com/huggingface/transformers/tree/master/examples/question-answering)     | SQuAD           | ✅ | ✅ | -  | -
 | [**`text-generation`**](https://github.com/huggingface/transformers/tree/master/examples/text-generation)           | -               | n/a | n/a | n/a | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/02_how_to_generate.ipynb)
 | [**`distillation`**](https://github.com/huggingface/transformers/tree/master/examples/distillation)                 | All             | - | -  | -  | -
-| [**`summarization`**](https://github.com/huggingface/transformers/tree/master/examples/seq2seq)     | CNN/Daily Mail    | -  | -  | ✅  | -
-| [**`translation`**](https://github.com/huggingface/transformers/tree/master/examples/seq2seq)         | WMT               | -  | -  | ✅  | -
+| [**`summarization`**](https://github.com/huggingface/transformers/tree/master/examples/seq2seq)                     | CNN/Daily Mail  | ✅  | -  | ✅  | -
+| [**`translation`**](https://github.com/huggingface/transformers/tree/master/examples/seq2seq)                       | WMT             | ✅  | -  | ✅  | -
 | [**`bertology`**](https://github.com/huggingface/transformers/tree/master/examples/bertology)                       | -               | - | -  | -  | -
 | [**`adversarial`**](https://github.com/huggingface/transformers/tree/master/examples/adversarial)                   | HANS            | ✅ | -  | -  | -
 

examples/seq2seq/README.md

@@ -192,6 +192,8 @@ model = AutoModelForSeq2SeqLM.from_pretrained(f'{output_dir}/best_tfmr')
 ### Fine-tuning using Seq2SeqTrainer
 To use `Seq2SeqTrainer` for fine-tuning you should use the `finetune_trainer.py` script. It subclasses `Trainer` to extend it for seq2seq training. Except the `Trainer` releated `TrainingArguments`, it shares the same argument names as that of `finetune.py` file. One notable difference is that, calculating generative metrics (BLEU, ROUGE) is optional and is controlled using the `--predict_with_generate` argument, set this argument to calculate BLEU and ROUGE metrics.
 
+With PyTorch 1.6+ it'll automatically use `native AMP` when `--fp16` is set. 
+
 To see all the possible command line options, run:
 
 ```bash