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.
Developed by: BITZ IT Consulting Ltd
Project: OpenCHS (Open Child Helpline System)
Funded by: UNICEF Venture Fund
License: Apache 2.0
Performance
Test Set (General Translation)
- BLEU: 0.2735
- chrF: 46.78
- Improvement over baseline: +0.0%
Domain Evaluation (Call Transcriptions)
- Domain BLEU: 0.0000
- Domain chrF: 2.93
- Domain COMET-QE: 0.0000
Intended Use
Primary Use Case: Translating Swahili helpline call transcriptions to English for case documentation, quality assurance, and cross-border referrals.
Languages: Swahili (source) → English (target)
Usage
from transformers import MarianTokenizer, MarianMTModel
model_name = "rogendo/mul-sw-en-translation-phase1"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
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: 8
Batch Size: 16
Learning Rate: 3e-05
Hardware: NVIDIA GPU with FP16 mixed precision
This model is part of the OpenCHS project supporting child helpline services across East Africa.
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