
# FSMT

## Model description

This is a ported version of fairseq-based [wmt16 transformer](https://github.com/jungokasai/deep-shallow/) for en-de.

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/wmt16-en-de-dist-12-1"
tokenizer = FSMTTokenizer.from_pretrained(mname)
model = FSMTForConditionalGeneration.from_pretrained(mname)

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

```

#### 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
-------|---------|----------
wmt16-en-de-dist-12-1  | 28.3 | 27.52

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=en-de
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/wmt16-en-de-dist-12-1 $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}
}
```


{"language": ["en", "de"], "license": "apache-2.0", "tags": ["translation", "wmt16", "allenai"], "datasets": ["wmt16"], "metrics": ["bleu"]}
transformers pytorch fsmt text2text-generation translation wmt16 allenai en de dataset:wmt16 arxiv:2006.10369 license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #fsmt #text2text-generation #translation #wmt16 #allenai #en #de #dataset-wmt16 #arxiv-2006.10369 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

FSMT
====


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


This is a ported version of fairseq-based wmt16 transformer for en-de.


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


All 3 models are available:


* wmt16-en-de-dist-12-1
* wmt16-en-de-dist-6-1
* wmt16-en-de-12-1


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:


model: wmt16-en-de-dist-12-1, fairseq: 28.3, transformers: 27.52


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:


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:


model: wmt16-en-de-dist-12-1, fairseq: 28.3, transformers: 27.52


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:


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


* training, etc.
* test set ### BibTeX entry and citation info
TAGS
#transformers #pytorch #fsmt #text2text-generation #translation #wmt16 #allenai #en #de #dataset-wmt16 #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:


model: wmt16-en-de-dist-12-1, fairseq: 28.3, transformers: 27.52


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:


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


* training, etc.
* test set ### BibTeX entry and citation info
passage: TAGS
#transformers #pytorch #fsmt #text2text-generation #translation #wmt16 #allenai #en #de #dataset-wmt16 #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:


model: wmt16-en-de-dist-12-1, fairseq: 28.3, transformers: 27.52


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:


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


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