Swahili-English Translation Model for Child Helpline Services
Model Description
This model is a fine-tuned version of Helsinki-NLP/opus-mt-mul-en for Swahili-to-English translation, specifically optimized for child helpline call transcriptions in East Africa (Tanzania, Uganda, Kenya).
Developed by: BITZ IT Consulting Ltd
Project: OpenCHS (Open Child Helpline System)
Funded by: UNICEF Venture Fund
License: Apache 2.0
Training Data
The model was fine-tuned on a combination of:
- NLLB Swahili-English parallel corpus (high quality, weight: 1.0)
- CCAligned web-crawled parallel data (supplementary, weight: 0.7)
- Total training samples: Approximately 50,000+ sentence pairs
- Domain focus: Conversational Swahili from helpline contexts
Performance
Test Set (General Translation)
- BLEU: 0.0195
- chrF: 18.28
- Improvement over baseline: +0.0%
Domain Evaluation (Call Transcriptions)
- Domain BLEU: 0.0000
- Domain chrF: 0.26
- Domain COMET-QE: 0.0000
Domain metrics are evaluated on real 10-minute call transcriptions from child helplines.
Intended Use
Primary Use Case: Translating Swahili helpline call transcriptions to English for:
- Case documentation and reporting
- Quality assurance and supervision
- Cross-border case referrals
- Data analysis and insights
Languages: Swahili (source) → English (target)
Limitations:
- Optimized for conversational Swahili (East African dialects)
- May not perform well on formal/literary Swahili
- Best for text lengths under 512 tokens
- Requires post-editing for critical use cases
Usage
from transformers import MarianTokenizer, MarianMTModel
model_name = "YOUR_USERNAME/brendaogutu/sw-en-translation-test-3"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
# Translate
swahili_text = "Habari za asubuhi. Ninaitwa Amina na nina miaka 14."
inputs = tokenizer(swahili_text, return_tensors="pt", padding=True)
outputs = model.generate(**inputs, num_beams=5, max_length=256)
translation = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(translation)
Training Details
Base Model: Helsinki-NLP/opus-mt-mul-en
Training Epochs: 0.1
Batch Size: 16 (effective: 64)
Learning Rate: 3e-05
Optimizer: AdamW
Hardware: NVIDIA GPU with FP16 mixed precision
Evaluation Methodology
- Test Set: Random 5% split from training distribution
- Domain Evaluation: Held-out set of real helpline call transcriptions (10-min calls)
- Metrics: BLEU, chrF, COMET-QE (quality estimation)
Citation
@software{openchs_translation_2025,
author = {BITZ IT Consulting Ltd},
title = {Swahili-English Translation Model for OpenCHS},
year = {2025},
publisher = {HuggingFace},
url = {https://huggingface.co/YOUR_USERNAME/brendaogutu/sw-en-translation-test-3}
}
Contact
For questions or issues, please contact: brenda@openchs.org
This model is part of the OpenCHS project supporting child helpline services across East Africa.
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