---
language:
- en
- el
tags:
- translation
license: cc-by-4.0
datasets:
- quickmt/quickmt-train.el-en
model-index:
- name: quickmt-en-el
  results:
  - task:
      name: Translation eng-ell
      type: translation
      args: eng-ell
    dataset:
      name: flores101-devtest
      type: flores_101
      args: eng_Latn ell_Grek devtest
    metrics:
    - name: BLEU
      type: bleu
      value: 28.86
    - name: CHRF
      type: chrf
      value: 54.94
    - name: COMET
      type: comet
      value: 88.87
---


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

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


## Model Information

* Trained using [`eole`](https://github.com/eole-nlp/eole)
* 195M parameter transformer 'big' with 8 encoder layers and 2 decoder layers
* 20k sentencepiece vocabularies
* Exported for fast inference to [CTranslate2](https://github.com/OpenNMT/CTranslate2) format
* Training data: https://huggingface.co/datasets/quickmt/quickmt-train.el-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 and download the model:

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

quickmt-model-download quickmt/quickmt-en-el ./quickmt-en-el
```

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-el/", device="auto")

# Translate - set beam size to 5 for higher quality (but slower speed)
sample_text = 'Dr. Ehud Ur, professor of medicine at Dalhousie University in Halifax, Nova Scotia and chair of the clinical and scientific division of the Canadian Diabetes Association cautioned that the research is still in its early days.'
t(sample_text, beam_size=5)
```

> 'Ο Δρ Ehud Ur, καθηγητής ιατρικής στο Πανεπιστήμιο Dalhousie στο Halifax της Νέας Σκωτίας και πρόεδρος του κλινικού και επιστημονικού τμήματος της Καναδικής Διαβητολογικής Εταιρείας, προειδοποίησε ότι η έρευνα βρίσκεται ακόμα στις πρώτες ημέρες της.'

```python
# 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)
```

> 'Ο Δρ. Ehud Ur, καθηγητής Ιατρικής στο Πανεπιστήμιο Dalhousie του Halifax της Νέας Σκωτίας και πρόεδρος του κλινικού και επιστημονικού τμήματος της Καναδικής Διαβητικής Εταιρείας, προειδοποίησε ότι η έρευνα παραμένει ακόμη σε πρώιμο στάδιο.'


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"->"ell_Grek"). `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 with a larger batch size).


|                                  |   bleu |   chrf2 |   comet22 |   Time (s) |
|:---------------------------------|-------:|--------:|----------:|-----------:|
| quickmt/quickmt-en-el            |  28.86 |   54.94 |     88.87 |       1.48 |
| Helsinki-NLP/opus-mt-en-el       |  25    |   52.16 |     86.9  |       4.63 |
| facebook/nllb-200-distilled-600M |  24.98 |   52.32 |     88.15 |      33.38 |
| facebook/nllb-200-distilled-1.3B |  27.36 |   54.43 |     89.35 |      58.82 |
| facebook/m2m100_418M             |  21.98 |   49.09 |     84.37 |      27.13 |
| facebook/m2m100_1.2B             |  26.59 |   53.09 |     88.18 |      52.98 |