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language:
- en
- de
tags:
- translation
license: cc-by-4.0
datasets:
- quickmt/quickmt-train.de-en
model-index:
- name: quickmt-de-en
results:
- task:
name: Translation deu-eng
type: translation
args: deu-eng
dataset:
name: flores101-devtest
type: flores_101
args: deu_Latn eng_Latn devtest
metrics:
- name: CHRF
type: chrf
value: 68.83
- name: BLEU
type: bleu
value: 44.20
- name: COMET
type: comet
value: 88.88
---
<a href="https://huggingface.co/spaces/quickmt/quickmt-gui"><img src="https://huggingface.co/datasets/huggingface/badges/resolve/main/open-in-hf-spaces-lg-dark.svg" alt="Open in Spaces"></a>
# `quickmt-de-en` Neural Machine Translation Model
`quickmt-de-en` is a reasonably fast and reasonably accurate neural machine translation model for translation from `de` into `en`.
## Model Information
* Trained using [`eole`](https://github.com/eole-nlp/eole)
* 185M parameter transformer 'big' with 8 encoder layers and 2 decoder layers
* 20k separate source/target Sentencepiece vocabulary
* Exported for fast inference to [CTranslate2](https://github.com/OpenNMT/CTranslate2) format
* Training data: https://huggingface.co/datasets/quickmt/quickmt-train.de-en/tree/main
See the `eole` model configuration in this repository for further details and the `eole-model` for the raw `eole` (pytorch) model.
## Usage with `quickmt`
You must install the Nvidia cuda toolkit first, if you want to do GPU inference.
Next, install the `quickmt` [python library](github.com/quickmt/quickmt).
```bash
git clone https://github.com/quickmt/quickmt.git
pip install ./quickmt/
```
Finally, use the model in python:
```python
from quickmt import Translator
from huggingface_hub import snapshot_download
# Download Model (if not downloaded already) and return path to local model
# Device is either 'auto', 'cpu' or 'cuda'
t = Translator(
snapshot_download("quickmt/quickmt-de-en", ignore_patterns="eole-model/*"),
device="cpu"
)
# Translate - set beam size to 5 for higher quality (but slower speed)
sample_text = 'Dr. Ehud Ur, Professor für Medizin an der Dalhousie University in Halifax, Nova Scotia, und Vorsitzender der Abteilung für Klinik und Wissenschaft des Kanadischen Diabetesverbands gab zu bedenken, dass die Forschungsarbeit noch in den Kinderschuhen stecke.'
t(sample_text, beam_size=5)
> 'Dr. Ehud Ur, a professor of medicine at Dalhousie University in Halifax, Nova Scotia, and chair of the Department of Clinic and Science of the Canadian Diabetes Association, said the research is still in its infancy.'
# Get alternative translations by sampling
# You can pass any cTranslate2 `translate_batch` arguments, e.g.
t([sample_text], sampling_temperature=1.2, beam_size=1, sampling_topk=50, sampling_topp=0.99)
```
The model is in `ctranslate2` format, and the tokenizers are `sentencepiece`, so you can use `ctranslate2` directly instead of through `quickmt`. It is also possible to get this model to work with e.g. [LibreTranslate](https://libretranslate.com/) which also uses `ctranslate2` and `sentencepiece`.
## Metrics
`bleu` and `chrf2` are calculated with [sacrebleu](https://github.com/mjpost/sacrebleu) on the [Flores200 `devtest` test set](https://huggingface.co/datasets/facebook/flores) ("deu_Latn"->"eng_Latn"). `comet22` with the [`comet`](https://github.com/Unbabel/COMET) library and the [default model](https://huggingface.co/Unbabel/wmt22-comet-da). "Time (s)" is the time in seconds to translate (using `ctranslate2`) the flores-devtest dataset (1012 sentences) on an RTX 4070s GPU with batch size 32 (faster speed is possible using a large batch size).
| | bleu | chrf2 | comet22 | Time (s) |
|:---------------------------------|-------:|--------:|----------:|-----------:|
| quickmt/quickmt-de-en | 44.21 | 68.83 | 88.89 | 1.03 |
| Helsink-NLP/opus-mt-de-en | 40.04 | 66.16 | 87.68 | 3.47 |
| facebook/nllb-200-distilled-600M | 42.46 | 67.07 | 88.14 | 21.36 |
| facebook/nllb-200-distilled-1.3B | 44.44 | 68.75 | 89.08 | 37.58 |
| facebook/m2m100_418M | 34.27 | 61.86 | 84.52 | 17.89 |
| facebook/m2m100_1.2B | 40.34 | 65.99 | 87.67 | 35.27 |
`quickmt-de-en` is the fastest and is higher quality than `opus-mt-de-en`, `m2m100_418m`, `m2m100_1.2B` and `nllb-200-distilled-600M`.
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