lbourdois's picture
Add multilingual to the language tag
- en
- de
- multilingual
license: apache-2.0
- translation
- wmt16
- allenai
- wmt16
- bleu
## 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
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
mname = "allenai/wmt16-en-de-dist-6-1"
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 gro�artig, 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](
## Eval results
Here are the BLEU scores:
model | fairseq | transformers
wmt16-en-de-dist-6-1 | 27.4 | 27.11
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:
git clone
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/
echo $PAIR
PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/ allenai/wmt16-en-de-dist-6-1 $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/ --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS
## Data Sources
- [training, etc.](
- [test set](
### BibTeX entry and citation info
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},