Swahili-English Translation Model

Model Details

• Pre-trained Model: Helsinki-NLP/opus-mt-en-sw
• Architecture: Transformer
• Training Data: Fine-tuned on 1,710,223 English-Swahili sentence pairs
• Base Model: Helsinki-NLP/opus-mt-en-sw
• 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 fine-tuned on a large dataset of English-Swahili sentence pairs, leveraging the Transformer architecture for effective translation.

• Developed by: Otieno Bildad Moses
• Model Type: Transformer
• Languages: Swahili, English
• License: Distributed under the MIT License

Training Data

The model was fine-tuned on the following dataset:

• OPUS-HPLT:
  • Package: en-sw.txt.zip
  • License: CC-BY-SA 4.0
  • Citation: Holger Schwenk et al., WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia, arXiv, July 2019.

Usage

Using a Pipeline as a High-Level Helper

from transformers import pipeline

# Initialize the translation pipeline
translator = pipeline("translation", model="Bildad/English-Swahili_Translation")

# Translate text
translation = translator("Hello Joshua?")[0]
translated_text = translation["translation_text"]

print(translated_text)