Translation
Transformers
PyTorch
English
Kinyarwanda
m2m_100
text2text-generation
Inference Endpoints
kin-eng / README.md
Remy
init
82bc937
---
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
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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