





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

import os
from pathlib import Path






def    _A	(   lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase ):
   a__						:    Tuple   		=	{
       "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]
   a__						:    List[str]   		=	{
       "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],
   }
   a__						:    Optional[int]   		=	F"""{src_lang}-{tgt_lang}"""

   a__						:    List[str]   		=	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=lowerCamelCase				,			exist_ok=lowerCamelCase )
   a__						:    Optional[Any]   		=	os.path.join(lowerCamelCase				,			"README.md" )
   print(F"""Generating {path}""" )
   with open(lowerCamelCase				,			"w"				,			encoding="utf-8" ) as f:
      f.write(lowerCamelCase )


# make sure we are under the root of the project
SCREAMING_SNAKE_CASE__							:      List[Any]         =    Path(__file__).resolve().parent.parent.parent
SCREAMING_SNAKE_CASE__							:      Tuple         =    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"]:
  SCREAMING_SNAKE_CASE__							:      Any         =    model_cards_dir / """allenai""" / model_name
  write_model_card(model_card_dir, src_lang="""en""", tgt_lang="""de""", model_name=model_name)








# Function to print upper half of diamond (pyramid)






def    _A	(   lowerCamelCase ):
   for i in range(0				,			lowerCamelCase ):
      for _ in range(0				,			n - i - 1 ):  # printing spaces
         print(" "				,			end="" )
      for _ in range(0				,			i + 1 ):  # printing stars
         print("* "				,			end="" )
      print()






def    _A	(   lowerCamelCase ):
   for i in range(lowerCamelCase				,			0				,			-1 ):
      for _ in range(lowerCamelCase				,			0				,			-1 ):  # printing stars
         print("* "				,			end="" )
      print()
      for _ in range(n - i + 1				,			0				,			-1 ):  # printing spaces
         print(" "				,			end="" )






def    _A	(   lowerCamelCase ):
   if n <= 0:
      print("       ...       ....        nothing printing :(" )
      return
   floyd(lowerCamelCase )  # upper half
   reverse_floyd(lowerCamelCase )  # lower half


if __name__ == "__main__":
  print(R"""| /\ | |- |  |-  |--| |\  /| |-""")
  print(R"""|/  \| |- |_ |_  |__| | \/ | |_""")
  SCREAMING_SNAKE_CASE__							:      Dict         =    1
  while K:
    SCREAMING_SNAKE_CASE__							:      str         =    int(input("""enter the number and , and see the magic : """))
    print()
    pretty_print(user_number)
    SCREAMING_SNAKE_CASE__							:      Tuple         =    int(input("""press 0 to exit... and 1 to continue..."""))

  print("""Good Bye...""")

