twb_nllb_finetuning

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Datasets:

• mbazaNLP/NMT_Tourism_parallel_data_en_kin
• mbazaNLP/NMT_Education_parallel_data_en_kin
• mbazaNLP/Kinyarwanda_English_parallel_dataset

Languages:

• en
• rw

library_name:

• transformers

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Model Details

Model Description

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 education-related data.

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

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:

• Education dataset or Tourism dataset (Depending on the downstream task)

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

Metrics

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

Results

Tourism metrics:

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

Education metrics:

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