README.md

---
language:
- en
- pl
tags:
- translation
license: cc-by-4.0
datasets:
- quickmt/quickmt-train.pl-en
model-index:
- name: quickmt-pl-en
  results:
  - task:
      name: Translation pol-eng
      type: translation
      args: pol-eng
    dataset:
      name: flores101-devtest
      type: flores_101
      args: ell_Grek eng_Latn devtest
    metrics:
    - name: BLEU
      type: bleu
      value: 27.46
    - name: CHRF
      type: chrf
      value: 57.18
    - name: COMET
      type: comet
      value: 85.04
---


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

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


## Try it on our Huggingface Space

Give it a try before downloading here: https://huggingface.co/spaces/quickmt/QuickMT-gui


## Model Information

* Trained using [`eole`](https://github.com/eole-nlp/eole) 
* 195M parameter transformer 'big' with 8 encoder layers and 2 decoder layers
* 20k separate Sentencepiece vocabs
* Expested for fast inference to [CTranslate2](https://github.com/OpenNMT/CTranslate2) format
* Training data: https://huggingface.co/datasets/quickmt/quickmt-train.pl-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-pl-en", ignore_patterns="eole-model/*"),
    device="cpu"
)

# Translate - set beam size to 1 for faster speed (but lower quality)
sample_text = 'Dr Ehud Ur, będący profesorem medycyny na Uniwersytecie Dalhousie w Halifaxie w Nowej Szkocji oraz przewodniczącym oddziału klinicznego i naukowego Kanadyjskiego Stowarzyszenia Cukrzycy, przestrzegł, iż badania nadal dopiero się zaczynają.'

t(sample_text, beam_size=5)
```

> 'Dr. Ehud Ur, a professor of medicine at Dalhousie University in Halifax, Nova Scotia and chairman of the clinical and scientific division of the Canadian Diabetes Association, warned that research is still just beginning.'

```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)
```

> 'Professor of Medicine at Dalhous University Halifax in Nova Scotia, MD and Chair of the Canadian Diabetes Association’s Clinical and Scientific Division, cautioned that research is just beginning.'

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`. A model in safetensors format to be used with `eole` is also provided.


## 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) ("pol_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 the flores-devtest dataset (1012 sentences) on an RTX 4070s GPU with batch size 32 (faster speed is possible using a larger batch size).

|                                  |   bleu |   chrf2 |   comet22 |   Time (s) |
|:---------------------------------|-------:|--------:|----------:|-----------:|
| quickmt/quickmt-pl-en            |  27.46 |   57.18 |     85.04 |       1.46 |
| Helsinki-NLP/opus-mt-pl-en       |  25.55 |   55.39 |     83.8  |       4.01 |
| facebook/nllb-200-distilled-600M |  29.28 |   57.11 |     84.65 |      21.61 |
| facebook/nllb-200-distilled-1.3B |  30.99 |   58.77 |     86.04 |      37.64 |
| facebook/m2m100_418M             |  22.12 |   52.51 |     80.41 |      17.99 |
| facebook/m2m100_1.2B             |  27.13 |   56.36 |     84.48 |      35.01 |