Create README.md
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README.md
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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 in two phases.
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#### Phase one:
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- General purpose dataset
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- Education dataset
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- Tourism dataset
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#### Phase two:
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- Education dataset
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Other than the dataset changes between phase one, and phase two finetuning; no other hyperparameters were modified. In both cases, the model was trained 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, and chrF++ metrics.
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### Results
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<!-- [More Information Needed] -->
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