---
language:
- be
- en
tags:
- translation
- pytorch
- transformers
- marian
pipeline_tag: translation
datasets:
- Helsinki-NLP/opus-100
base_model: Helsinki-NLP/opus-mt-mul-en
metrics:
- bleu
---

# Belarusian to English Translator (be-en)

This model is a fine-tuned version of [Helsinki-NLP/opus-mt-mul-en](https://huggingface.co/Helsinki-NLP/opus-mt-mul-en) for translating text from **Belarusian (be)** to **English (en)**.

## Model Description
The model was fine-tuned using the `transformers` library on the Belarusian-English split of the [OPUS-100 dataset](https://huggingface.co/datasets/Helsinki-NLP/opus-100). It is based on the MarianMT architecture and is optimized for quick and accurate translation of short to medium-length sentences.

## Example of usage

You can use this model directly with the `transformers` library:

```python
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

# Load model and tokenizer
model_name = "Aleton/be-en-translator"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

# Set device
device = "cuda" if torch.cuda.is_available() else "cpu"
model = model.to(device)

# Text to translate
text = "Прывітанне, як справы?"

# Generate translation
inputs = tokenizer(text, return_tensors="pt").to(device)
outputs = model.generate(**inputs, max_length=128)
translated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(translated_text)
# Expected output: Hello, how are you?
```