mbazaNLP/NMT_Education_parallel_data_en_kin
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How to use mbazaNLP/Nllb_finetuned_education_en_kin with Transformers:
# Use a pipeline as a high-level helper
# Warning: Pipeline type "translation" is no longer supported in transformers v5.
# You must load the model directly (see below) or downgrade to v4.x with:
# 'pip install "transformers<5.0.0'
from transformers import pipeline
pipe = pipeline("translation", model="mbazaNLP/Nllb_finetuned_education_en_kin") # Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("mbazaNLP/Nllb_finetuned_education_en_kin")
model = AutoModelForSeq2SeqLM.from_pretrained("mbazaNLP/Nllb_finetuned_education_en_kin")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.
Use the code below to get started with the model.
The model was finetuned on three datasets; a general purpose dataset, a tourism, and an education dataset.
The model was finetuned in two phases.
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.
Model performance was measured using BLEU, spBLEU, TER, and chrF++ 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 |