Model Details

Model Description

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This is a Machine Translation model, finetuned from NLLB-200's distilled 1.3B model, it is meant to be used in machine translation for tourism-related data, in a Rwandan context.

• Finetuning code repository: the code used to finetune this model can be found here

Quantization details

The model is quantized to 8-bit precision using the Ctranslate2 library.

pip install ctranslate2

Using the command:

ct2-transformers-converter --model <model-dir> --quantization int8 --output_dir <output-model-dir>

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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 purpose dataset, a tourism, and an education 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

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Metrics

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

Results