



'''simple docstring'''






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

import os
from pathlib import Path


def 				snake_case_  (      _lowerCAmelCase						:		Optional[Any]     ,   _lowerCAmelCase						:		Optional[int]     ,   _lowerCAmelCase						:		Any     ,   _lowerCAmelCase						:		Union[str, Any]						)   ->     Optional[int]:
							UpperCAmelCase				: 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]
							UpperCAmelCase				: List[str]											=	{
							    '''wmt16-en-de-dist-12-1''': [2_8.3, 2_7.5_2],
							    '''wmt16-en-de-dist-6-1''': [2_7.4, 2_7.1_1],
							    '''wmt16-en-de-12-1''': [2_6.9, 2_5.7_5],
							}
							UpperCAmelCase				: List[str]											=	f"""{src_lang}-{tgt_lang}"""

							UpperCAmelCase				: 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						)
							UpperCAmelCase				: Dict											=	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
UpperCamelCase__:     Optional[Any]										=    Path(__file__).resolve().parent.parent.parent
UpperCamelCase__:     Dict										=    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"]:
						UpperCamelCase__:     List[str]										=    model_cards_dir / "allenai" / model_name
						write_model_card(model_card_dir, src_lang="en", tgt_lang="de", model_name=model_name)










'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase__:     Dict										=    logging.get_logger(__name__)

UpperCamelCase__:     int										=    {
    # See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}

class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	lowerCamelCase__    				= """megatron-bert"""







	def __init__(		self    :  int	,    __snake_case    :  List[Any]=29056	,    __snake_case    :  Dict=1024	,    __snake_case    :  Tuple=24	,    __snake_case    :  Any=16	,    __snake_case    :  Tuple=4096	,    __snake_case    :  int="gelu"	,    __snake_case    :  Dict=0.1	,    __snake_case    :  str=0.1	,    __snake_case    :  Optional[Any]=512	,    __snake_case    :  Tuple=2	,    __snake_case    :  Tuple=0.02	,    __snake_case    :  int=1E-12	,    __snake_case    :  Union[str, Any]=0	,    __snake_case    :  int="absolute"	,    __snake_case    :  str=True	,    **__snake_case    :  Tuple	,    )    ->	Union[str, Any]:
								super().__init__(pad_token_id=__snake_case	,    **__snake_case	)

								UpperCAmelCase				: Any											=	vocab_size
								UpperCAmelCase				: Tuple											=	hidden_size
								UpperCAmelCase				: List[Any]											=	num_hidden_layers
								UpperCAmelCase				: str											=	num_attention_heads
								UpperCAmelCase				: Optional[int]											=	hidden_act
								UpperCAmelCase				: Optional[int]											=	intermediate_size
								UpperCAmelCase				: Dict											=	hidden_dropout_prob
								UpperCAmelCase				: List[Any]											=	attention_probs_dropout_prob
								UpperCAmelCase				: Dict											=	max_position_embeddings
								UpperCAmelCase				: Any											=	type_vocab_size
								UpperCAmelCase				: str											=	initializer_range
								UpperCAmelCase				: str											=	layer_norm_eps
								UpperCAmelCase				: Optional[Any]											=	position_embedding_type
								UpperCAmelCase				: str											=	use_cache





