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quickmt-hu-en
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---
language:
- en
- hu
tags:
- translation
license: cc-by-4.0
datasets:
- quickmt/quickmt-train.hu-en
model-index:
- name: quickmt-hu-en
  results:
  - task:
      name: Translation hun-eng
      type: translation
      args: hun-eng
    dataset:
      name: flores101-devtest
      type: flores_101
      args: hun_Latn eng_Latn devtest
    metrics:
    - name: BLEU
      type: bleu
      value: 35.03
    - name: CHRF
      type: chrf
      value: 62.44
    - name: COMET
      type: comet
      value: 87.78
---


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

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


## 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.hu-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-hu-en ./quickmt-hu-en
```

Finally use the model in python:

```python
from quickmt import Translator

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

# Translate - set beam size to 1 for faster speed (but lower quality)
sample_text = 'Dr. Ehud Ur, az új-Skóciai Halifaxban lévő Dalhousie Egyetem orvosprofesszora és a Kanadai Diabétesz Társaság klinikai és tudományos részlegének elnöke figyelmeztetett, hogy a kutatás még korai szakaszában tart.'

t(sample_text, beam_size=5)
```

> 'Dr. Ehud Ur, professor of medicine at Dalhousie University in Halifax, Nova Scotia and president of the Clinical and Scientific Division of the Canadian Diabetes Society, warned that the research was still in its early stages.'

```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 Dr. Ehud Ur, a professor of medicine at Dalhousie University in Halifax, Nova Scotia, and President of Clinical and Scientific Division of the Canadian Diabetes Company said the study would still be in its early stages.'

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) ("hun_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).

## hu -> en flores-devtest metrics

|                                  |   bleu |   chrf2 |   comet22 |   Time (s) |
|:---------------------------------|-------:|--------:|----------:|-----------:|
| quickmt/quickmt-hu-en            |  35.03 |   62.44 |     87.78 |       1.24 |
| Helsinki-NLP/opus-mt-hu-en       |  30.92 |   59.48 |     86.34 |       3.65 |
| facebook/nllb-200-distilled-600M |  33.29 |   60.48 |     86.82 |      21.19 |
| facebook/nllb-200-distilled-1.3B |  35.91 |   62.63 |     88.21 |      37.45 |
| facebook/m2m100_418M             |  27.22 |   56.36 |     83.56 |      18.05 |
| facebook/m2m100_1.2B             |  33.08 |   60.71 |     86.93 |      35.6  |