---
title: wmt19-de-en-6-6-big
canonical_url: "https://www.modelscope.cn/models/LLM-Research/wmt19-de-en-6-6-big"
md_url: "https://www.modelscope.cn/models/LLM-Research/wmt19-de-en-6-6-big.md"
repository: LLM-Research/wmt19-de-en-6-6-big
last_updated: 2024-09-08
license: apache-2.0
pipeline_tag: translation
tasks:
  - translation
model_type:
  - fsmt
architectures:
  - FSMTForConditionalGeneration
library_name:
  - transformer
  - pytorch
frameworks:
  - pytorch
language:
  - de
  - en
downloads: 2144
stars: 0
tags:
  - translation
  - wmt19
  - allenai
---

# wmt19-de-en-6-6-big

> wmt19-de-en-6-6-big - LLM-Research 在 ModelScope 开源的模型。This is a ported version of fairseq-based wmt19 transformer for de-en.

LLM-Research/wmt19-de-en-6-6-big 是 ModelScope 魔搭社区上的translation模型，采用 apache-2.0 许可。

- **Repository**: LLM-Research/wmt19-de-en-6-6-big
- **License**: apache-2.0
- **Tasks**: translation
- **Tags**: translation, wmt19, allenai
- **Downloads**: 2144
- **Stars**: 0
- **Last updated**: 2024-09-08

Source: https://www.modelscope.cn/models/LLM-Research/wmt19-de-en-6-6-big

---

# 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-big"
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-big  |  39.9

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-big $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}
}
```