---
language:
- en
- fr
tags:
- translation
license: cc-by-4.0
datasets:
- quickmt/quickmt-train.en-fr
model-index:
- name: quickmt-en-fr
  results:
  - task:
      name: Translation fra-eng
      type: translation
      args: fra-eng
    dataset:
      name: flores101-devtest
      type: flores_101
      args: eng_Latn fra_Latn devtest
    metrics:
    - name: CHRF
      type: chrf
      value: 71.60
    - name: BLEU
      type: bleu
      value: 50.79
    - name: COMET
      type: comet
      value: 87.11
---


# `quickmt-en-fr` Neural Machine Translation Model 

`quickmt-en-fr` is a reasonably fast and reasonably accurate neural machine translation model for translation from `en` into `fr`.


## Model Information

* Trained using [`eole`](https://github.com/eole-nlp/eole)
* 185M parameter transformer 'big' with 8 encoder layers and 2 decoder layers
* 50k joint Sentencepiece vocabulary
* Exported for fast inference to [CTranslate2](https://github.com/OpenNMT/CTranslate2) format
* Training data: https://huggingface.co/datasets/quickmt/quickmt-train.fr-en/tree/main

See the `eole-config.yaml` model configuration in this repository for further details. 


## Usage with `quickmt`

You must install the Nvidia cuda toolkit first, if you want to do GPU inference.

Next, install the `quickmt` python library and download the model:

```bash
git clone https://github.com/quickmt/quickmt.git
pip install ./quickmt/

# List available models
quickmt-list

# Download a model
quickmt-model-download quickmt/quickmt-en-fr ./quickmt-en-fr
```

Finally use the model in python:

```python
from quickmt import Translator

# Auto-detects GPU, set to "cpu" to force CPU inference
t = Translator("./quickmt-en-fr/", device="auto")

# Translate - set beam size to 5 for higher quality (but slower speed)
sample_text = "The Virgo interferometer is a large-scale scientific instrument near Pisa, Italy, for detecting gravitational waves."
t(sample_text, beam_size=1)

# Get alternative translations by sampling
# You can pass any cTranslate2 `translate_batch` arguments
t([sample_text], sampling_temperature=1.2, beam_size=1, sampling_topk=50, sampling_topp=0.9)
```

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) ("eng_Latn"->"fra_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.

| Model                            | chrf2 | bleu    | comet22 | Time (s) |
| -------------------------------- | ----- | ------- | ------- | -------- |
| quickmt/quickmt-en-fr            | 71.60 | 50.79   | 87.11   |  1.28    |
| Helsinki-NLP/opus-mt-en-fr       | 69.98 | 47.97   | 86.29   |  4.13    |
| facebook/m2m100_418M             | 63.29 | 39.52   | 82.11   | 22.4     |
| facebook/m2m100_1.2B             | 68.31 | 45.39   | 86.50   | 44.0     |
| facebook/nllb-200-distilled-600M | 70.36 | 48.71   | 87.63   | 27.8     |
| facebook/nllb-200-distilled-1.3B | 71.95 | 51.10   | 88.50   | 47.8     |

`quickmt-en-fr` is the fastest and is higher quality than `opus-mt-en-fr`, `m2m100_418m`, `m2m100_1.2B`.