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/allenai/wmt16-en-de-dist-6-1/README.md

README.md

@@ -0,0 +1,103 @@
+
+---
+language:
+- en
+- de
+thumbnail:
+tags:
+- translation
+- wmt16
+- allenai
+license: apache-2.0
+datasets:
+- wmt16
+metrics:
+- bleu
+---
+
+# FSMT
+
+## Model description
+
+This is a ported version of fairseq-based [wmt16 transformer](https://github.com/jungokasai/deep-shallow/) for en-de.
+
+For more details, please, see [Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation](https://arxiv.org/abs/2006.10369).
+
+All 3 models are available:
+
+* [wmt16-en-de-dist-12-1](https://huggingface.co/allenai/wmt16-en-de-dist-12-1)
+* [wmt16-en-de-dist-6-1](https://huggingface.co/allenai/wmt16-en-de-dist-6-1)
+* [wmt16-en-de-12-1](https://huggingface.co/allenai/wmt16-en-de-12-1)
+
+
+## Intended uses & limitations
+
+#### How to use
+
+```python
+from transformers import FSMTForConditionalGeneration, FSMTTokenizer
+mname = "allenai/wmt16-en-de-dist-6-1"
+tokenizer = FSMTTokenizer.from_pretrained(mname)
+model = FSMTForConditionalGeneration.from_pretrained(mname)
+
+input = "Machine learning is great, isn't it?"
+input_ids = tokenizer.encode(input, return_tensors="pt")
+outputs = model.generate(input_ids)
+decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(decoded) # Maschinelles Lernen ist großartig, nicht wahr?
+
+```
+
+#### Limitations and bias
+
+
+## Training data
+
+Pretrained weights were left identical to the original model released by allenai. For more details, please, see the [paper](https://arxiv.org/abs/2006.10369).
+
+## Eval results
+
+Here are the BLEU scores:
+
+model   | fairseq | transformers
+-------|---------|----------
+wmt16-en-de-dist-6-1  | 27.4 | 27.11
+
+The score is slightly below the score reported in the paper, as the researchers don't use `sacrebleu` and measure the score on tokenized outputs. `transformers` score was measured using `sacrebleu` on detokenized outputs.
+
+The score was calculated using this code:
+
+```bash
+git clone https://github.com/huggingface/transformers
+cd transformers
+export PAIR=en-de
+export DATA_DIR=data/$PAIR
+export SAVE_DIR=data/$PAIR
+export BS=8
+export NUM_BEAMS=5
+mkdir -p $DATA_DIR
+sacrebleu -t wmt16 -l $PAIR --echo src > $DATA_DIR/val.source
+sacrebleu -t wmt16 -l $PAIR --echo ref > $DATA_DIR/val.target
+echo $PAIR
+PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py allenai/wmt16-en-de-dist-6-1 $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
+```
+
+## Data Sources
+
+- [training, etc.](http://www.statmt.org/wmt16/)
+- [test set](http://matrix.statmt.org/test_sets/newstest2016.tgz?1504722372)
+
+
+### BibTeX entry and citation info
+
+```
+@misc{kasai2020deep,
+    title={Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation},
+    author={Jungo Kasai and Nikolaos Pappas and Hao Peng and James Cross and Noah A. Smith},
+    year={2020},
+    eprint={2006.10369},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+