



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

import os
from pathlib import Path


def 	_A	(   _UpperCamelCase      ,					_UpperCamelCase      ,					_UpperCamelCase      ,					_UpperCamelCase			):
				_UpperCAmelCase						:  int     					=	{
				    '''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]
				_UpperCAmelCase						:  List[str]     					=	{
				    '''wmt19-de-en-6-6-base''': [0, 38.37],
				    '''wmt19-de-en-6-6-big''': [0, 39.90],
				}
				_UpperCAmelCase						:  Optional[int]     					=	F'''{src_lang}-{tgt_lang}'''

				_UpperCAmelCase						:  Tuple     					=	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=_UpperCamelCase      ,					exist_ok=_UpperCamelCase			)
				_UpperCAmelCase						:  Union[str, Any]     					=	os.path.join(_UpperCamelCase      ,					'''README.md'''			)
				print(F'''Generating {path}'''			)
				with open(_UpperCamelCase      ,					'''w'''      ,					encoding='''utf-8'''			) as f:
								f.write(_UpperCamelCase			)


# make sure we are under the root of the project
UpperCAmelCase__			:   int      			=    Path(__file__).resolve().parent.parent.parent
UpperCAmelCase__			:   Tuple      			=    repo_dir / 'model_cards'

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





UpperCAmelCase__			:   Optional[int]      			=    [0.4_85, 0.4_56, 0.4_06]
UpperCAmelCase__			:   Union[str, Any]      			=    [0.2_29, 0.2_24, 0.2_25]
UpperCAmelCase__			:   Union[str, Any]      			=    [0.5, 0.5, 0.5]
UpperCAmelCase__			:   Optional[Any]      			=    [0.5, 0.5, 0.5]
UpperCAmelCase__			:   str      			=    [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]
UpperCAmelCase__			:   Dict      			=    [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]
