transformers pytorch m2m_100 text2text-generation translation en rw dataset:mbazaNLP/NMT_Tourism_parallel_data_en_kin dataset:mbazaNLP/NMT_Education_parallel_data_en_kin dataset:mbazaNLP/Kinyarwanda_English_parallel_dataset license:cc-by-2.0 autotrain_compatible endpoints_compatible region:us
---
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
license: cc-by-2.0
pipeline_tag: translation
---
## Model Details

### Model Description

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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.



- **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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## 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:
- Education 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

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#### Metrics

Model performance was measured using BLEU, spBLEU, TER, and chrF++ metrics.

### Results

|Lang. Direction| BLEU |  spBLEU  | chrf++ |TER   |
|:----|:----:|:----:|:----:|----:|
| Eng -> Kin     | 45.96   | 59.20 | 68.79 |   41.61    |
| Kin -> Eng     | 43.98   |  44.94  |   63.05 |  41.41     |

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