luo_swa_translation_model

Overview

THiNK’s Luo–Swahili Translation Model is a fine-tuned T5-small designed to translate between the Luo (dav) and Swahili (swa) languages. It was trained on a parallel corpus of approximately 29,300 Luo–Swahili sentence pairs drawn from the larger “Kenyan Low-Resource Language Data” dataset. thinkKenya—also known as Tech Innovators Network Kenya—is a community-driven technology initiative founded in 2019 to support digital transformation and applied open innovation across Kenya, with a special focus on African local languages.

Model Details

• Model name: thinkKenya/luo_swa_translation_model
• Architecture: T5-small (≈60.5 M parameters; base checkpoint: google/t5-small)
• Framework: Hugging Face Transformers (weights in safetensors format)
• Tensor type: fp32
• Training status: In progress (latest reported step: 146,600)

Dataset

• Dataset name: thinkKenya/kenyan-low-resource-language-data
• Task: Translation (parallel text, Parquet format)
• Languages: Luo (ISO dav) ↔ Swahili (ISO swa)
• Subset used: luo_swa (≈29.3 k total examples; train split: 21.3 k; test split: 5.33 k)
• License: CC BY 4.0

Organization: Tech Innovators Network Kenya (thinkKenya)

• Website: think.ke
• Hugging Face Org: thinkKenya
• Founded: 2019
• Mission: To accelerate digital transformation and applied open innovation in Kenya, with special emphasis on building AI solutions for African local languages.

Training Configuration

Example Usage

from transformers import T5Tokenizer, T5ForConditionalGeneration

tokenizer = T5Tokenizer.from_pretrained("thinkKenya/luo_swa_translation_model")
model     = T5ForConditionalGeneration.from_pretrained("thinkKenya/luo_swa_translation_model")

input_text = "translate Luo to Swahili: Wuki ghwa choki"
inputs     = tokenizer(input_text, return_tensors="pt")
outputs    = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Limitations

• Ongoing fine-tuning: Outputs may still be unstable until training completes.
• Domain coverage: Trained on conversational and narrative sentences—performance may drop on highly specialized or out-of-domain text.
• No public benchmarks yet: Users are encouraged to evaluate with their own BLEU/ROUGE metrics.

Thought for a couple of seconds

Below is the License section added to the model card, specifying the CC BY 4.0 terms and the required attribution format.

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License

This model and the underlying dataset are released under the Creative Commons Attribution 4.0 International License (CC BY 4.0).

You are free to:

• Share — copy and redistribute the material in any medium or format
• Adapt — remix, transform, and build upon the material for any purpose, even commercially

Under the following terms:

• Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made.
• No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

Suggested attribution for this model:

“Luo–Swahili Translation Model, thinkKenya (Tech Innovators Network Kenya), CC BY 4.0, https://huggingface.co/thinkKenya/luo_swa_translation_model”

Citation

@misc{luo_swa_translation_model,
  title        = {Luo–Swahili Translation Model},
  author       = {thinkKenya (Tech Innovators Network Kenya)},
  year         = {2024},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/thinkKenya/luo_swa_translation_model}},
}