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---
language:
- fa
- en
tags:
- translation
license: cc-by-4.0
datasets:
- quickmt/quickmt-train.fa-en
- quickmt/madlad400-en-backtranslated-fa
- quickmt/newscrawl2024-en-backtranslated-fa
model-index:
- name: quickmt-fa-en
results:
- task:
name: Translation pes-eng
type: translation
args: pes-eng
dataset:
name: flores101-devtest
type: flores_101
args: pes_Arab eng_Latn devtest
metrics:
- name: BLEU
type: bleu
value: 38.99
- name: CHRF
type: chrf
value: 64.55
- name: COMET
type: comet
value: 88.14
---
# `quickmt-fa-en` Neural Machine Translation Model
`quickmt-fa-en` is a reasonably fast and reasonably accurate neural machine translation model for translation from `fa` into `en`.
`quickmt` models are roughly 3 times faster for GPU inference than OpusMT models and roughly [40 times](https://huggingface.co/spaces/quickmt/quickmt-vs-libretranslate) faster than [LibreTranslate](https://huggingface.co/spaces/quickmt/quickmt-vs-libretranslate)/[ArgosTranslate](github.com/argosopentech/argos-translate).
## *UPDATED VERSION!*
This model was trained with back-translated data and has improved translation quality!
## Try it on our Huggingface Space
Give it a try before downloading here: https://huggingface.co/spaces/quickmt/QuickMT-Demo
## Model Information
* Trained using [`eole`](https://github.com/eole-nlp/eole)
* 200M parameter seq2seq transformer
* 32k separate Sentencepiece vocabs
* Exported for fast inference to [CTranslate2](https://github.com/OpenNMT/CTranslate2) format
* The pytorch model (for use with [`eole`](https://github.com/eole-nlp/eole)) is available in this repository in the `eole-model` folder
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 -e ./quickmt/
quickmt-model-download quickmt/quickmt-fa-en ./quickmt-fa-en
```
Finally use the model in python:
```python
from quickmt import Translator
# Auto-detects GPU, set to "cpu" to force CPU inference
mt = Translator("./quickmt-ar-en/", device="auto")
# Translate - set beam size to 1 for faster speed (but lower quality)
sample_text = 'دکتر ایهود اور، استاد پزشکی دانشگاه دالهاوزی در هلیفکس، نوااسکوشیا و رئیس بخش کلینیکی و علمی انجمن دیابت کانادا هشدار داد که این تحقیق هنوز در روزهای آغازین خود می\u200cباشد.'
mt(sample_text, beam_size=5)
```
> 'Dr. Ehud Orr, a professor of medicine at Dalhousie University in Halifax, Nova Scotia, and head of the Canadian Diabetes Association’s clinical and scientific department, warned that the research is still in its early days.'
```python
# Get alternative translations by sampling
# You can pass any cTranslate2 `translate_batch` arguments
mt([sample_text], sampling_temperature=1.2, beam_size=1, sampling_topk=50, sampling_topp=0.9)
```
> 'Dr. Ehud Orr, medical professor of Dalhousie University in Halifax, Nova Scotia and head of the Clinical and Scientific Section of the Canadian Diabetes Association warned that the research is still in its early days.'
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). `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.
| | bleu | chrf2 | comet22 | Time (s) |
|:--------------------------------------|-------:|--------:|----------:|-----------:|
| quickmt/quickmt-fa-en | 38.99 | 64.55 | 88.14 | 1.1 |
| facebook/nllb-200-distilled-600M | 34.8 | 60.86 | 86.49 | 21.13 |
| facebook/nllb-200-distilled-1.3B | 37.91 | 63.39 | 87.82 | 36.86 |
| facebook/m2m100_418M | 27.2 | 55.82 | 82.9 | 18.23 |
| facebook/m2m100_1.2B | 29.13 | 56.4 | 83.5 | 34.8 |
| tencent/HY-MT1.5-1.8B | 20.87 | 55.02 | 86.22 | 10.0 |
| tencent/HY-MT1.5-7B-FP8 | 28.16 | 59.49 | 88.07 | 36.0 |
| CohereLabs/aya-expanse-8b (bnb quant) | 35.29 | 62.46 | 88.43 | 77.37 |
|