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Migrate model card from transformers-repo

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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/wmt19-de-en-6-6-big/README.md

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+
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+ ---
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+
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+ language:
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+ - de
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+ - en
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+ thumbnail:
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+ tags:
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+ - translation
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+ - wmt19
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+ - allenai
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+ license: apache-2.0
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+ datasets:
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+ - wmt19
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+ metrics:
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+ - bleu
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+ ---
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+
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+ # FSMT
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+
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+ ## Model description
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+
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+ This is a ported version of fairseq-based [wmt19 transformer](https://github.com/jungokasai/deep-shallow/) for de-en.
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+
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+ For more details, please, see [Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation](https://arxiv.org/abs/2006.10369).
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+
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+ 2 models are available:
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+
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+ * [wmt19-de-en-6-6-big](https://huggingface.co/allenai/wmt19-de-en-6-6-big)
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+ * [wmt19-de-en-6-6-base](https://huggingface.co/allenai/wmt19-de-en-6-6-base)
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+
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+
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+ ## Intended uses & limitations
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+
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+ #### How to use
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+
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+ ```python
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+ from transformers import FSMTForConditionalGeneration, FSMTTokenizer
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+ mname = "allenai/wmt19-de-en-6-6-big"
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+ tokenizer = FSMTTokenizer.from_pretrained(mname)
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+ model = FSMTForConditionalGeneration.from_pretrained(mname)
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+
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+ input = "Maschinelles Lernen ist großartig, nicht wahr?"
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+ input_ids = tokenizer.encode(input, return_tensors="pt")
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+ outputs = model.generate(input_ids)
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+ decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
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+ print(decoded) # Machine learning is great, isn't it?
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+
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+ ```
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+
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+ #### Limitations and bias
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+
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+
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+ ## Training data
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+
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+ 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).
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+
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+ ## Eval results
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+
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+ Here are the BLEU scores:
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+
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+ model | transformers
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+ -------|---------
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+ wmt19-de-en-6-6-big | 39.9
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+
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+ The score was calculated using this code:
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+
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+ ```bash
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+ git clone https://github.com/huggingface/transformers
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+ cd transformers
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+ export PAIR=de-en
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+ export DATA_DIR=data/$PAIR
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+ export SAVE_DIR=data/$PAIR
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+ export BS=8
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+ export NUM_BEAMS=5
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+ mkdir -p $DATA_DIR
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+ sacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source
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+ sacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target
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+ echo $PAIR
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+ PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py allenai/wmt19-de-en-6-6-big $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
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+ ```
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+
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+ ## Data Sources
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+
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+ - [training, etc.](http://www.statmt.org/wmt19/)
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+ - [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561)
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+
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+
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+ ### BibTeX entry and citation info
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+
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+ ```
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+ @misc{kasai2020deep,
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+ title={Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation},
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+ author={Jungo Kasai and Nikolaos Pappas and Hao Peng and James Cross and Noah A. Smith},
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+ year={2020},
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+ eprint={2006.10369},
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+ archivePrefix={arXiv},
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+ primaryClass={cs.CL}
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+ }
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+ ```
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+