Convert 12-1 and 6-1 en-de models from AllenNLP

https://github.com/jungokasai/deep-shallow#download-trained-deep-shallow-models

• These should be FSMT models, so can be part of #6940 or done after.
• They should be uploaded to the AllenNLP namespace. If stas takes this, they can start in stas/ and I will move them.
• model card(s) should link to the original repo and paper.
• I hope same en-de tokenizer already ported.
• Would be interesting to compare BLEU to the initial models in that PR. There is no ensemble so we should be able to reported scores pretty well.
• Ideally this requires 0 lines of checked in python code, besides maybe an integration test.

Desired Signature:

model = FSMT.from_pretrained('allen_nlp/en-de-12-1')

Weights can be downloaded with gdown https://pypi.org/project/gdown/

pip install gdown
gdown https://drive.google.com/uc?id=1x_G2cjvM1nW5hjAB8-vWxRqtQTlmIaQU

@stas00 if you are blocked in the late stages of #6940 and have extra cycles, you could give this a whirl. We could also wait for that to be finalized and then either of us can take this.

[sshleifer]

New model’s config should be {'num_beams':5} according to https://github.com/jungokasai/deep-shallow#evaluation

[sshleifer]

• since different dataset, different val set, which implies that 28 and 41 are not comparable BLEU scores.
• These models should be significantly faster than the Marian models at similar performance levels.
• We can finetune them on the new data if we think that will help.
• FYI Helinki-NLP/opus-mt-en-de trained on way more data than the fairseq model I think, not totally sure.