
# FSMT

## Model description

This is a ported version of fairseq-based [wmt19 transformer](https://github.com/jungokasai/deep-shallow/) for de-en.

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/wmt19-de-en-6-6-base"
tokenizer = FSMTTokenizer.from_pretrained(mname)
model = FSMTForConditionalGeneration.from_pretrained(mname)

input = "Maschinelles Lernen ist großartig, nicht wahr?"
input_ids = tokenizer.encode(input, return_tensors="pt")
outputs = model.generate(input_ids)
decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(decoded) # Machine learning is great, isn't it?

```

#### 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
-------|---------
wmt19-de-en-6-6-base  |  38.37

The score was calculated using this code:

```bash
git clone https://github.com/huggingface/transformers
cd transformers
export PAIR=de-en
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/wmt19-de-en-6-6-base $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}
}
```


{"language": ["de", "en"], "license": "apache-2.0", "tags": ["translation", "wmt19", "allenai"], "datasets": ["wmt19"], "metrics": ["bleu"]}
transformers pytorch fsmt text2text-generation translation wmt19 allenai de en dataset:wmt19 arxiv:2006.10369 license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #fsmt #text2text-generation #translation #wmt19 #allenai #de #en #dataset-wmt19 #arxiv-2006.10369 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

FSMT
====


Model description
-----------------


This is a ported version of fairseq-based wmt19 transformer for de-en.


For more details, please, see Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation.


2 models are available:


* wmt19-de-en-6-6-big
* wmt19-de-en-6-6-base


Intended uses & limitations
---------------------------


#### How to use


#### Limitations and bias


Training data
-------------


Pretrained weights were left identical to the original model released by allenai. For more details, please, see the paper.


Eval results
------------


Here are the BLEU scores:



The score was calculated using this code:


Data Sources
------------


* training, etc.
* test set


### BibTeX entry and citation info



#### How to use #### Limitations and bias


Training data
-------------


Pretrained weights were left identical to the original model released by allenai. For more details, please, see the paper.


Eval results
------------


Here are the BLEU scores:



The score was calculated using this code:


Data Sources
------------


* training, etc.
* test set ### BibTeX entry and citation info
TAGS
#transformers #pytorch #fsmt #text2text-generation #translation #wmt19 #allenai #de #en #dataset-wmt19 #arxiv-2006.10369 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 #### How to use #### Limitations and bias


Training data
-------------


Pretrained weights were left identical to the original model released by allenai. For more details, please, see the paper.


Eval results
------------


Here are the BLEU scores:



The score was calculated using this code:


Data Sources
------------


* training, etc.
* test set ### BibTeX entry and citation info
passage: TAGS
#transformers #pytorch #fsmt #text2text-generation #translation #wmt19 #allenai #de #en #dataset-wmt19 #arxiv-2006.10369 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
#### How to use#### Limitations and bias


Training data
-------------


Pretrained weights were left identical to the original model released by allenai. For more details, please, see the paper.


Eval results
------------


Here are the BLEU scores:



The score was calculated using this code:


Data Sources
------------


* training, etc.
* test set### BibTeX entry and citation info