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---
license: cc-by-2.0
datasets:
- mbazaNLP/NMT_Tourism_parallel_data_en_kin
- mbazaNLP/NMT_Education_parallel_data_en_kin
- mbazaNLP/Kinyarwanda_English_parallel_dataset
language:
- en
- rw
library_name: transformers
---
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
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.
- **Finetuning code repository:** the code used to finetune this model can be found [here](https://github.com/Digital-Umuganda/twb_nllb_finetuning)
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
## How to Get Started with the Model
Use the code below to get started with the model.
### Training Procedure
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.
The model was finetuned in two phases.
#### Phase one:
- General purpose dataset
- Education dataset
- Tourism dataset
#### Phase two:
- Tourism dataset
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.
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
#### Testing Data
<!-- This should link to a Data Card if possible. -->
#### Metrics
Model performance was measured using BLEU, spBLEU, TER, and chrF++ metrics.
### Results
|Lang. Direction| BLEU | spBLEU | chrf++ |TER |
|:----|:----:|:----:|:----:|----:|
| Eng -> Kin | 28.37 | 40.62 | 56.48 | 59.71 |
| Kin -> Eng | 42.54 | 44.84 | 61.54 | 43.87 |
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