---
license: mit
library_name: transformers
---

# Swahili-English Translation Model

## Model Details

- **Pre-trained Model**: Rogendo/sw-en
- **Architecture**: Transformer
- **Training Data**: Trained on 210,000 Swahili-English corpus pairs
- **Base Model**: Helsinki-NLP/opus-mt-en-swc
- **Training Method**: Fine-tuned with an emphasis on bidirectional translation between Swahili and English.

### Model Description

This Swahili-English translation model was developed to handle translations between Swahili, one of Africa's most spoken languages, and English. It was trained on a diverse dataset sourced from OPUS, leveraging the Transformer architecture for effective translation.

- **Developed by:** Peter Rogendo, Frederick Kioko
- **Model Type:** Transformer
- **Languages:** Swahili, English
- **License:** Distributed under the MIT License

### Training Data

The model was fine-tuned on the following datasets:
  
  - **WikiMatrix:**
    - **Package**: WikiMatrix.en-sw in Moses format
    - **License**: [CC-BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/legalcode)
    - **Citation**: Holger Schwenk et al., WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia, arXiv, July 2019.

  - **ParaCrawl:**
    - **Package**: ParaCrawl.en-sw in Moses format
    - **License**: [CC0](http://paracrawl.eu/download.html)
    - **Acknowledgement**: Please acknowledge the ParaCrawl project at [ParaCrawl](http://paracrawl.eu).

  - **TICO-19:**
    - **Package**: tico-19.en-sw in Moses format
    - **License**: [CC0](https://tico-19.github.io/LICENSE.md)
    - **Citation**: J. Tiedemann, 2012, Parallel Data, Tools, and Interfaces in OPUS.

## Usage

### Using a Pipeline as a High-Level Helper

```python
from transformers import pipeline

# Initialize the translation pipeline
translator = pipeline("translation", model="Bildad/Swahili-English_Translation")

# Translate text
translation = translator("Habari yako?")[0]
translated_text = translation["translation_text"]

print(translated_text)