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--- |
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license: cc-by-2.0 |
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datasets: |
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- mbazaNLP/NMT_Tourism_parallel_data_en_kin |
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- mbazaNLP/NMT_Education_parallel_data_en_kin |
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- mbazaNLP/Kinyarwanda_English_parallel_dataset |
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language: |
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- en |
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- rw |
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library_name: transformers |
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--- |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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This is a Machine Translation model, finetuned from [NLLB](https://huggingface.co/facebook/nllb-200-distilled-1.3B)-200's distilled 1.3B model, it is meant to be used in machine translation for education-related data. |
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- **Finetuning code repository:** the code used to finetune this model can be found [here](https://github.com/Digital-Umuganda/twb_nllb_finetuning) |
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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### Training Procedure |
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The model was finetuned on three datasets; a [general](https://huggingface.co/datasets/mbazaNLP/Kinyarwanda_English_parallel_dataset) purpose dataset, a [tourism](https://huggingface.co/datasets/mbazaNLP/NMT_Tourism_parallel_data_en_kin), and an [education](https://huggingface.co/datasets/mbazaNLP/NMT_Education_parallel_data_en_kin) dataset. |
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The model was finetuned on an A100 40GB GPU for two epochs. |
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## Evaluation |
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<!-- This section describes the evaluation protocols and provides the results. --> |
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#### Testing Data |
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<!-- This should link to a Data Card if possible. --> |
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#### Metrics |
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Model performance was measured using BLEU, spBLEU, TER, and chrF++ metrics. |
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### Results |
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