Instructions to use NHNDQ/nllb-finetuned-en2ko with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NHNDQ/nllb-finetuned-en2ko 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="NHNDQ/nllb-finetuned-en2ko")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("NHNDQ/nllb-finetuned-en2ko") model = AutoModelForSeq2SeqLM.from_pretrained("NHNDQ/nllb-finetuned-en2ko") - Inference
- Notebooks
- Google Colab
- Kaggle
Model Details
- Model Description: Fine-tuned facebook/nllb-200-distilled-600M model
- Developed by: Jisu Kim, Juhwan Lee, TakSung Heo, and Minsu Jeong
- Model Type: Translation
- Language(s):
- Source Language: English
- Target Language: Korean
- License: CC-BY-4.0
Dataset
BLEU Score
- Deepl translation: 22.83
- Fine-tune nllb: 33.66
Uses
This model can be used for translation and text-to-text generation
Data Augmentation with backtranslation task
You can exercise korean data augmentation task with python package KoTAN
- Downloads last month
- 4,688