"ru":"Машинное обучение - это здорово, не так ли?",
"de":"Maschinelles Lernen ist großartig, nicht wahr?",
}
# BLUE scores as follows:
# "pair": [fairseq, transformers]
scores={
"wmt16-en-de-dist-12-1":[28.3,27.52],
"wmt16-en-de-dist-6-1":[27.4,27.11],
"wmt16-en-de-12-1":[26.9,25.75],
}
pair=f"{src_lang}-{tgt_lang}"
readme=f"""
---
language:
- {src_lang}
- {tgt_lang}
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 {src_lang}-{tgt_lang}.
For more details, please, see [Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation](https://arxiv.org/abs/2006.10369).
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).
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.