---
language:
  - en
  - rto
  - lug
  - ach
  - nyn
license: mit
tags:
  - translation
  - african-languages
  - rutooro
  - luganda
  - acholi
  - runyankore
datasets:
  - custom
metrics:
  - bleu
library_name: transformers
pipeline_tag: translation
base_model: Helsinki-NLP/opus-mt-en-mul
widget:
  - text: ">>rutooro<< Education is important for community development."
  - text: ">>luganda<< Mobile phones have transformed communication in rural areas."
  - text: ">>acholi<< The market opens early in the morning."
  - text: ">>runyankore<< Women play a crucial role in community development."
---


# Rutooro-Centric Multilingual Translation Model

This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-mul](https://huggingface.co/Helsinki-NLP/opus-mt-en-mul) that specializes in translating from English to Rutooro and other East African languages.

## Model Description

This translation model focuses specifically on Rutooro while maintaining high quality for other East African languages including Luganda, Acholi, and Runyankore. It was fine-tuned on a carefully curated dataset containing thousands of translation pairs across multiple languages, with special emphasis on rows where Rutooro translations were present.

## Supported Languages

The model primarily supports translation from English to:

- **Rutooro** (Ugandan language spoken by the Batooro people)
- **Luganda** (Most widely spoken Ugandan language)
- **Acholi** (Nilotic language spoken in Northern Uganda and South Sudan)
- **Runyankore** (Language spoken in southwestern Uganda)

Other languages from the base model may also work but with varying quality.

## Usage

To use this model for translation:

```python
from transformers import pipeline

# Initialize the translation pipeline
translator = pipeline("translation", model="MubarakB/rutooro-multilingual-translator")

# Translate to Rutooro
text = "Education is important for community development."
rutooro_translation = translator(f">>rutooro<< {text}")
print(f"Rutooro: {rutooro_translation[0]['translation_text']}")

# Translate to other supported languages
luganda_translation = translator(f">>luganda<< {text}")
print(f"Luganda: {luganda_translation[0]['translation_text']}")

acholi_translation = translator(f">>acholi<< {text}")
print(f"Acholi: {acholi_translation[0]['translation_text']}")

runyankore_translation = translator(f">>runyankore<< {text}")
print(f"Runyankore: {runyankore_translation[0]['translation_text']}")
```

### Language Tokens

When using this model, you must prefix your input text with the appropriate language token:

- `>>rutooro<<` - For Rutooro translation
- `>>luganda<<` - For Luganda translation
- `>>acholi<<` - For Acholi translation
- `>>runyankore<<` - For Runyankore translation

## Example Translations

| English | Rutooro | Luganda | Acholi | Runyankore |
|---------|---------|---------|--------|------------|
| Education is important for development. | Okusoma nikwomuhendo ahabw'okukulaakulana. | Okusoma kikulu nnyo mu nkulaakulana. | Kwan dongo pire me yubo lobo. | Okushoma nikukuru ahabw'okukulaakulana. |
| Mobile phones have transformed communication in rural areas. | Esimu zabyemikono zihindwireho enkoragana omubicweka byakyaro. | Essimu ezitambulizibwa mu ngalo zikyusizza eby'empuliziganya mu byalo. | Simu latic me cing ocele kit me kwat lok i gang me tung. | Amasimu g'ebyemikono gakyusizza empuliziganya mu byalo. |
| The market opens early in the morning. | Akatale kagurwaho kare omumakya. | Akatale kabbika mu makya. | Gang cuk yabedo labongo ikare me ice. | Akatale kakingirweho makya. |
| Women play a crucial role in community development. | Abakazzi nibakora mulimo gwa mughaso ngu kukulakulanya ekyaro. | Abakazi balina ekifo ekikulu mu nkulaakulana y'eggwanga. | Mon ni tii tic ma kwako alokaloka me kom kin gang. | Abakazi bakola omulimu murungi mu nkulaakulana y'ekitundu. |

## Model Details

- **Base Model:** Helsinki-NLP/opus-mt-en-mul
- **Model Type:** Sequence-to-Sequence (Encoder-Decoder Transformer)
- **Training Data:** Multilingual dataset with focus on Rutooro translations
- **Fine-tuning:** Targeted fine-tuning with special emphasis on Rutooro language pairs
- **Languages Coverage:**
  - Rutooro (11.75% of dataset)
  - Luganda (99.86% of dataset)
  - Acholi (99.87% of dataset)
  - Runyankore (99.87% of dataset)

## Limitations

- The model is optimized for general conversational text and may not perform as well on highly specialized or technical content
- Performance may vary based on language coverage in the training data
- Quality can vary based on sentence complexity and domain
- Some languages may benefit from additional fine-tuning with more domain-specific data

## Citation

If you use this model in your research, please cite:

```bibtex
@misc{rutooro-multilingual-translator,
  author = {Mubarak Bachu},
  title = {Rutooro-Centric Multilingual Translation Model},
  year = {2025},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/MubarakB/rutooro-multilingual-translator}}
}
```

## Acknowledgments

This model builds upon the excellent work by Helsinki-NLP and the Opus-MT project. Special thanks to the communities supporting the preservation and computational processing of East African languages.