




'''simple docstring'''







# Usage:
# ./gen-card-allenai-wmt19.py

import os
from pathlib import Path






def 		_lowercase		(     lowercase_						,							lowercase_						,							lowercase_						,							lowercase_  ):



		'''simple docstring'''







		_snake_case     	=	{
		    "en": "Machine learning is great, isn't it?",
		    "ru": "Машинное обучение - это здорово, не так ли?",
		    "de": "Maschinelles Lernen ist großartig, nicht wahr?",
		}

		# BLUE scores as follows:
		# "pair": [fairseq, transformers]
		_snake_case     	=	{
		    "wmt19-de-en-6-6-base": [0, 38.37],
		    "wmt19-de-en-6-6-big": [0, 39.90],
		}
		_snake_case     	=	f'''{src_lang}-{tgt_lang}'''

		_snake_case     	=	f'''
---

language:
- {src_lang}
- {tgt_lang}
thumbnail:
tags:
- translation
- wmt19
- allenai
license: apache-2.0
datasets:
- wmt19
metrics:
- bleu
---

# FSMT

## Model description

This is a ported version of fairseq-based [wmt19 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).

2 models are available:

* [wmt19-de-en-6-6-big](https://huggingface.co/allenai/wmt19-de-en-6-6-big)
* [wmt19-de-en-6-6-base](https://huggingface.co/allenai/wmt19-de-en-6-6-base)


## Intended uses & limitations

#### How to use

```python
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
mname = "allenai/{model_name}"
tokenizer = FSMTTokenizer.from_pretrained(mname)
model = FSMTForConditionalGeneration.from_pretrained(mname)

input = "{texts[src_lang]}"
input_ids = tokenizer.encode(input, return_tensors="pt")
outputs = model.generate(input_ids)
decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(decoded) # {texts[tgt_lang]}

```

#### 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   |  transformers
-------|---------
{model_name}  |  {scores[model_name][1]}

The score was calculated using this code:

```bash
git clone https://github.com/huggingface/transformers
cd transformers
export PAIR={pair}
export DATA_DIR=data/$PAIR
export SAVE_DIR=data/$PAIR
export BS=8
export NUM_BEAMS=5
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 allenai/{model_name} $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/wmt19/)
- [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561)


### 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}}
}}
```

'''
		model_card_dir.mkdir(parents=lowercase_						,							exist_ok=lowercase_  )
		_snake_case     	=	os.path.join(lowercase_						,							"README.md"  )
		print(f'''Generating {path}'''  )
		with open(lowercase_						,							"w"						,							encoding="utf-8"  ) as f:
				f.write(lowercase_  )


# make sure we are under the root of the project
_lowerCamelCase							:						Optional[Any]							     =  Path(__file__).resolve().parent.parent.parent
_lowerCamelCase							:						List[Any]							     =  repo_dir / 'model_cards'

for model_name in ["wmt19-de-en-6-6-base", "wmt19-de-en-6-6-big"]:
						_lowerCamelCase							:						int							     =  model_cards_dir / 'allenai' / model_name
						write_model_card(model_card_dir, src_lang='de', tgt_lang='en', model_name=model_name)











'''simple docstring'''







def 		_lowercase		(     lowercase_						,							lowercase_ = 2  ):



		'''simple docstring'''







		return round((float(lowercase_  ) * 9 / 5) + 32						,							lowercase_  )






def 		_lowercase		(     lowercase_						,							lowercase_ = 2  ):



		'''simple docstring'''







		return round(float(lowercase_  ) + 273.15						,							lowercase_  )






def 		_lowercase		(     lowercase_						,							lowercase_ = 2  ):



		'''simple docstring'''







		return round((float(lowercase_  ) * 9 / 5) + 491.67						,							lowercase_  )






def 		_lowercase		(     lowercase_						,							lowercase_ = 2  ):



		'''simple docstring'''







		return round((float(lowercase_  ) - 32) * 5 / 9						,							lowercase_  )






def 		_lowercase		(     lowercase_						,							lowercase_ = 2  ):



		'''simple docstring'''







		return round(((float(lowercase_  ) - 32) * 5 / 9) + 273.15						,							lowercase_  )






def 		_lowercase		(     lowercase_						,							lowercase_ = 2  ):



		'''simple docstring'''







		return round(float(lowercase_  ) + 459.67						,							lowercase_  )






def 		_lowercase		(     lowercase_						,							lowercase_ = 2  ):



		'''simple docstring'''







		return round(float(lowercase_  ) - 273.15						,							lowercase_  )






def 		_lowercase		(     lowercase_						,							lowercase_ = 2  ):



		'''simple docstring'''







		return round(((float(lowercase_  ) - 273.15) * 9 / 5) + 32						,							lowercase_  )






def 		_lowercase		(     lowercase_						,							lowercase_ = 2  ):



		'''simple docstring'''







		return round((float(lowercase_  ) * 9 / 5)						,							lowercase_  )






def 		_lowercase		(     lowercase_						,							lowercase_ = 2  ):



		'''simple docstring'''







		return round((float(lowercase_  ) - 491.67) * 5 / 9						,							lowercase_  )






def 		_lowercase		(     lowercase_						,							lowercase_ = 2  ):



		'''simple docstring'''







		return round(float(lowercase_  ) - 459.67						,							lowercase_  )






def 		_lowercase		(     lowercase_						,							lowercase_ = 2  ):



		'''simple docstring'''







		return round((float(lowercase_  ) * 5 / 9)						,							lowercase_  )






def 		_lowercase		(     lowercase_						,							lowercase_ = 2  ):



		'''simple docstring'''







		return round((float(lowercase_  ) * 1.25 + 273.15)						,							lowercase_  )






def 		_lowercase		(     lowercase_						,							lowercase_ = 2  ):



		'''simple docstring'''







		return round((float(lowercase_  ) * 2.25 + 32)						,							lowercase_  )






def 		_lowercase		(     lowercase_						,							lowercase_ = 2  ):



		'''simple docstring'''







		return round((float(lowercase_  ) * 1.25)						,							lowercase_  )






def 		_lowercase		(     lowercase_						,							lowercase_ = 2  ):



		'''simple docstring'''







		return round((float(lowercase_  ) * 2.25 + 32 + 459.67)						,							lowercase_  )


if __name__ == "__main__":
						import doctest

						doctest.testmod()





