Update README.md

README.md

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----
-library_name: transformers
-license: cc-by-nc-4.0
-base_model: SalomonMetre13/nllb-luo-swa-mt-v1
-tags:
-- generated_from_trainer
-model-index:
-- name: nllb-luo-swa-mt-v1
-  results: []
----
+# Model Card for nllb-luo-swa-mt-v1
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
+## Model Overview
 
-# nllb-luo-swa-mt-v1
+**Model Name**: nllb-luo-swa-mt-v1  
+**Model Type**: Machine Translation (Luo (Dholuo) to Swahili)  
+**Base Model**: NLLB-200-distilled-600M  
+**Languages**: Luo (Dholuo), Swahili  
+**Version**: 1.0  
+**License**: CC0 (Public Domain)  
+**Dataset**: [SalomonMetre13/luo_swa_arXiv_2501.11003](https://huggingface.co/datasets/SalomonMetre13/luo_swa_arXiv_2501.11003)  
 
-This model is a fine-tuned version of [SalomonMetre13/nllb-luo-swa-mt-v1](https://huggingface.co/SalomonMetre13/nllb-luo-swa-mt-v1) on an unknown dataset.
-It achieves the following results on the evaluation set:
-- eval_loss: 0.1146
-- eval_bleu: 19.64
-- eval_runtime: 798.5876
-- eval_samples_per_second: 3.665
-- eval_steps_per_second: 0.917
-- epoch: 0.4556
-- step: 3000
+This model is a fine-tuned version of the `NLLB-200-distilled-600M` model for translation between Luo (Dholuo) and Swahili. It was trained on a parallel corpus derived from the Dholuo–Swahili corpus created by Mbogho et al. (2025), based on community-driven data collection efforts.
 
-## Model description
+## Model Description
 
-More information needed
+The `nllb-luo-swa-mt-v1` model performs machine translation from Luo (Dholuo) to Swahili, designed to improve translation capabilities for these low-resource languages. It was fine-tuned using the parallel corpus from the paper **"Building low-resource African language corpora: A case study of Kidawida, Kalenjin and Dholuo"** by Mbogho et al. (2025). This model is particularly valuable for promoting linguistic diversity and facilitating the development of Natural Language Processing (NLP) tools in African languages.
 
-## Intended uses & limitations
+### Key Features:
+- **Training Data**: Fine-tuned on the Dholuo–Swahili parallel text corpus from the dataset [SalomonMetre13/luo_swa_arXiv_2501.11003](https://huggingface.co/datasets/SalomonMetre13/luo_swa_arXiv_2501.11003), derived from the grassroots data collection effort by Mbogho et al. (2025).
+- **Performance**: Achieved a BLEU score of 21.56 on the evaluation set, showing strong performance in a low-resource setting.
+- **Qualitative Analysis**: Translations generated by this model are sometimes more fluent and accurate than the provided reference translations.
+  
+## Intended Use
 
-More information needed
+This model can be used for machine translation applications between Luo (Dholuo) and Swahili. Potential use cases include:
+- **Educational tools**: Enabling educational content in both languages, aiding language learners and teachers.
+- **Public health and community development**: Translating health information, community messages, and official communications.
+- **Cultural preservation**: Supporting the preservation and growth of the Luo language in the digital age.
 
-## Training and evaluation data
+## Model Evaluation
 
-More information needed
+The model was evaluated using the BLEU score, which is commonly used to assess machine translation performance. A BLEU score of 21.56 was achieved, which is a strong result for a low-resource language pair. Qualitative analysis of the translations suggests that, in some cases, the model's outputs outperform the reference translations in terms of fluency and accuracy.
 
-## Training procedure
+## Training Details
 
-### Training hyperparameters
+- **Training Data**: The model was trained on the Dholuo–Swahili parallel corpus, based on the dataset [SalomonMetre13/luo_swa_arXiv_2501.11003](https://huggingface.co/datasets/SalomonMetre13/luo_swa_arXiv_2501.11003) derived from Mbogho et al.'s (2025) work. The corpus includes text translations and is publicly available for further use and improvement.
+- **Model Architecture**: The model is fine-tuned from the `NLLB-200-distilled-600M` version of the NLLB model family, which is designed for multilingual translation tasks.
 
-The following hyperparameters were used during training:
-- learning_rate: 3e-05
-- train_batch_size: 4
-- eval_batch_size: 4
-- seed: 42
-- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
-- lr_scheduler_type: linear
-- lr_scheduler_warmup_steps: 200
-- num_epochs: 10
-- mixed_precision_training: Native AMP
+## Limitations
 
-### Framework versions
+- **Low-Resource Context**: While the model performs well given the limited amount of data, its performance may still lag behind models trained on larger corpora for more widely spoken languages.
+- **Domain-Specific Use**: The model may require additional fine-tuning to perform optimally on domain-specific text such as medical, legal, or technical content.
 
-- Transformers 4.51.3
-- Pytorch 2.6.0+cu124
-- Datasets 3.5.1
-- Tokenizers 0.21.1
+## Future Directions
+
+- **Expanding the Dataset**: The quality and coverage of the model could be improved by incorporating more diverse and larger datasets.
+- **Additional Language Pairs**: Further fine-tuning of the model to support other language pairs involving Luo and Swahili could make the model even more versatile.
+- **Real-World Applications**: The model could be applied to real-world projects such as translating educational materials, public health information, or community communication platforms.
+
+## Acknowledgements
+
+This model was developed based on the Dholuo–Swahili parallel corpus created by Mbogho et al.\ (2025) as part of their work in building low-resource African language corpora. The corpus was made publicly available on platforms like Zenodo and Mozilla Common Voice.
+
+## How to Use
+
+You can access the model via the Hugging Face Hub at:  
+[https://huggingface.co/SalomonMetre13/nllb-luo-swa-mt-v1](https://huggingface.co/SalomonMetre13/nllb-luo-swa-mt-v1)
+
+To load the model using the Hugging Face `transformers` library, use the following code:
+
+```python
+from transformers import pipeline
+
+translator = pipeline("translation", model="SalomonMetre13/nllb-luo-swa-mt-v1")
+translation = translator("Ninapenda kujua kuhusu lugha ya Dholuo.")
+print(translation)