


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

import os
from pathlib import Path




def       a__						(		snake_case__				:       List[Any]	,      snake_case__				:       Tuple	,      snake_case__				:       Optional[Any]	,      snake_case__				:       int    ):
  _UpperCAmelCase		:       Optional[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		:       List[str]       =    f'''{src_lang}-{tgt_lang}'''

  _UpperCAmelCase		:       Optional[int]       =    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=snake_case__	,      exist_ok=snake_case__    )
  _UpperCAmelCase		:       Tuple       =    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
SCREAMING_SNAKE_CASE__				:    Tuple         =    Path(__file__).resolve().parent.parent.parent
SCREAMING_SNAKE_CASE__				:    Union[str, Any]         =    repo_dir / 'model_cards'

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









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE__				:    List[str]         =    {
    'configuration_flaubert': ['FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FlaubertConfig', 'FlaubertOnnxConfig'],
    'tokenization_flaubert': ['FlaubertTokenizer'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE__				:    int         =    [
     'FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'FlaubertForMultipleChoice',
     'FlaubertForQuestionAnswering',
     'FlaubertForQuestionAnsweringSimple',
     'FlaubertForSequenceClassification',
     'FlaubertForTokenClassification',
     'FlaubertModel',
     'FlaubertWithLMHeadModel',
     'FlaubertPreTrainedModel',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE__				:    Dict         =    [
     'TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFFlaubertForMultipleChoice',
     'TFFlaubertForQuestionAnsweringSimple',
     'TFFlaubertForSequenceClassification',
     'TFFlaubertForTokenClassification',
     'TFFlaubertModel',
     'TFFlaubertPreTrainedModel',
     'TFFlaubertWithLMHeadModel',
 ]


if TYPE_CHECKING:
 from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
 from .tokenization_flaubert import FlaubertTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flaubert import (
      FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      FlaubertForMultipleChoice,
      FlaubertForQuestionAnswering,
      FlaubertForQuestionAnsweringSimple,
      FlaubertForSequenceClassification,
      FlaubertForTokenClassification,
      FlaubertModel,
      FlaubertPreTrainedModel,
      FlaubertWithLMHeadModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_flaubert import (
      TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFFlaubertForMultipleChoice,
      TFFlaubertForQuestionAnsweringSimple,
      TFFlaubertForSequenceClassification,
      TFFlaubertForTokenClassification,
      TFFlaubertModel,
      TFFlaubertPreTrainedModel,
      TFFlaubertWithLMHeadModel,
  )

else:
 import sys

 SCREAMING_SNAKE_CASE__				:    List[Any]         =    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





