Migrate model card from transformers-repo

Read announcement at https://discuss.huggingface.co/t/announcement-all-model-cards-will-be-migrated-to-hf-co-model-repos/2755
Original file history: https://github.com/huggingface/transformers/commits/master/model_cards/facebook/wmt19-de-en/README.md

README.md

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+---
+language: 
+- de
+- en
+tags:
+- translation
+- wmt19
+- facebook
+license: apache-2.0
+datasets:
+- wmt19
+metrics:
+- bleu
+thumbnail: https://huggingface.co/front/thumbnails/facebook.png
+---
+
+# FSMT
+
+## Model description
+
+This is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for de-en.
+
+For more details, please see, [Facebook FAIR's WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616).
+
+The abbreviation FSMT stands for FairSeqMachineTranslation
+
+All four models are available:
+
+* [wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru)
+* [wmt19-ru-en](https://huggingface.co/facebook/wmt19-ru-en)
+* [wmt19-en-de](https://huggingface.co/facebook/wmt19-en-de)
+* [wmt19-de-en](https://huggingface.co/facebook/wmt19-de-en)
+
+## Intended uses & limitations
+
+#### How to use
+
+```python
+from transformers import FSMTForConditionalGeneration, FSMTTokenizer
+mname = "facebook/wmt19-de-en"
+tokenizer = FSMTTokenizer.from_pretrained(mname)
+model = FSMTForConditionalGeneration.from_pretrained(mname)
+
+input = "Maschinelles Lernen ist großartig, oder?"
+input_ids = tokenizer.encode(input, return_tensors="pt")
+outputs = model.generate(input_ids)
+decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(decoded) # Machine learning is great, isn't it?
+
+```
+
+#### Limitations and bias
+
+- The original (and this ported model) doesn't seem to handle well inputs with repeated sub-phrases, [content gets truncated](https://discuss.huggingface.co/t/issues-with-translating-inputs-containing-repeated-phrases/981)
+
+## Training data
+
+Pretrained weights were left identical to the original model released by fairseq. For more details, please, see the [paper](https://arxiv.org/abs/1907.06616).
+
+## Eval results
+
+pair   | fairseq | transformers
+-------|---------|----------
+de-en  | [42.3](http://matrix.statmt.org/matrix/output/1902?run_id=6750) | 41.35
+
+The score is slightly below the score reported by `fairseq`, since `transformers`` currently doesn't support:
+- model ensemble, therefore the best performing checkpoint was ported (``model4.pt``).
+- re-ranking
+
+The score was calculated using this code:
+
+```bash
+git clone https://github.com/huggingface/transformers
+cd transformers
+export PAIR=de-en
+export DATA_DIR=data/$PAIR
+export SAVE_DIR=data/$PAIR
+export BS=8
+export NUM_BEAMS=15
+mkdir -p $DATA_DIR
+sacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source
+sacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target
+echo $PAIR
+PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS
+```
+note: fairseq reports using a beam of 50, so you should get a slightly higher score if re-run with `--num_beams 50`.
+
+## Data Sources
+
+- [training, etc.](http://www.statmt.org/wmt19/)
+- [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561)
+
+
+### BibTeX entry and citation info
+
+```bibtex
+@inproceedings{...,
+  year={2020},
+  title={Facebook FAIR's WMT19 News Translation Task Submission},
+  author={Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey},
+  booktitle={Proc. of WMT},
+}
+```
+
+
+## TODO
+
+- port model ensemble (fairseq uses 4 model checkpoints)
+