| BLEU Score | Translation Result | |
|---|---|---|
| Korean ➡️ English | 45.148 | KE-T5-Ko2En-Base Inference Result |
| English ➡️ Korean | - |
- Evaluation script is on metric.py
- Korean ➡️ English Result evaluated on 553500 sentence pairs which are disjoint from the train set.
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
# Korean -> English Machine Translation
tokenizer = AutoTokenizer.from_pretrained("QuoQA-NLP/KE-T5-Ko2En-Base")
model = AutoModelForSeq2SeqLM.from_pretrained("QuoQA-NLP/KE-T5-Ko2En-Base")
# English -> Korean Machine Translation
tokenizer = AutoTokenizer.from_pretrained("QuoQA-NLP/KE-T5-En2Ko-Base")
model = AutoModelForSeq2SeqLM.from_pretrained("QuoQA-NLP/KE-T5-En2Ko-Base")- For batch translation, please refer to inference.py.
- P100 16GB supports inferencing of 250 pairs per batch on device.
- A100 40GB supports inferencing of 600 pairs per batch on device.
- For single sentence translation, please refer to inference_single.py.