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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

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>

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

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

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Datasets used to train mbazaNLP/Quantized_Nllb_Finetuned_Edu_En_Kin_8bit