





# Usage:
# ./gen-card-allenai-wmt16.py

import os
from pathlib import Path

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:Dict      ,				snake_case__	:Union[str, Any]      ,				snake_case__	:Union[str, Any]      ,				snake_case__	:int  )   ->    Optional[int]:
  _lowercase						    =			{
      '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]
  _lowercase						    =			{
      '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],
  }
  _lowercase						    =			F"""{src_lang}-{tgt_lang}"""

  _lowercase						    =			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).

All 3 models are available:

* [wmt16-en-de-dist-12-1](https://huggingface.co/allenai/wmt16-en-de-dist-12-1)
* [wmt16-en-de-dist-6-1](https://huggingface.co/allenai/wmt16-en-de-dist-6-1)
* [wmt16-en-de-12-1](https://huggingface.co/allenai/wmt16-en-de-12-1)


## 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   | fairseq | transformers
-------|---------|----------
{model_name}  | {scores[model_name][0]} | {scores[model_name][1]}

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.

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 wmt16 -l $PAIR --echo src > $DATA_DIR/val.source
sacrebleu -t wmt16 -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/wmt16/)
- [test set](http://matrix.statmt.org/test_sets/newstest2016.tgz?1504722372)


### 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=snake_case__      ,				exist_ok=snake_case__  )
  _lowercase						    =			os.path.join(snake_case__      ,				'README.md'  )
  print(F"""Generating {path}"""  )
  with open(snake_case__      ,				'w'      ,				encoding='utf-8'  ) as f:
    f.write(snake_case__  )


# make sure we are under the root of the project
snake_case								=							Path(__file__).resolve().parent.parent.parent
snake_case								=							repo_dir / """model_cards"""

for model_name in ["wmt16-en-de-dist-12-1", "wmt16-en-de-dist-6-1", "wmt16-en-de-12-1"]:
      snake_case								=							model_cards_dir / """allenai""" / model_name
      write_model_card(model_card_dir, src_lang="""en""", tgt_lang="""de""", model_name=model_name)

def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     int | float | str				)			->						tuple[int, int]:
       try:
              SCREAMING_SNAKE_CASE_							:				int		=float(UpperCAmelCase_				)
       except ValueError:
              raise ValueError('''Please enter a valid number'''				)
       SCREAMING_SNAKE_CASE_							:				Any		=decimal - int(UpperCAmelCase_				)
       if fractional_part == 0:
              return int(UpperCAmelCase_				), 1
       else:
              SCREAMING_SNAKE_CASE_							:				Any		=len(str(UpperCAmelCase_				).split('''.'''				)[1]				)
              SCREAMING_SNAKE_CASE_							:				str		=int(decimal * (1_0**number_of_frac_digits)				)
              SCREAMING_SNAKE_CASE_							:				Any		=1_0**number_of_frac_digits
              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Tuple		=denominator, numerator
              while True:
                     SCREAMING_SNAKE_CASE_							:				Any		=dividend % divisor
                     if remainder == 0:
                            break
                     SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Optional[int]		=divisor, remainder
              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Optional[int]		=numerator / divisor, denominator / divisor
              return int(UpperCAmelCase_				), int(UpperCAmelCase_				)


if __name__ == "__main__":
 print(F"{decimal_to_fraction(2) = }")
 print(F"{decimal_to_fraction(89.0) = }")
 print(F"{decimal_to_fraction('67') = }")
 print(F"{decimal_to_fraction('45.0') = }")
 print(F"{decimal_to_fraction(1.5) = }")
 print(F"{decimal_to_fraction('6.25') = }")
 print(F"{decimal_to_fraction('78td') = }")
