Instructions to use ding-diri-ding-dong/opus-mt-ko-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ding-diri-ding-dong/opus-mt-ko-en 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="ding-diri-ding-dong/opus-mt-ko-en")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ding-diri-ding-dong/opus-mt-ko-en") model = AutoModelForSeq2SeqLM.from_pretrained("ding-diri-ding-dong/opus-mt-ko-en", device_map="auto") - Notebooks
- Google Colab
- Kaggle
opus-mt-ko-en
This model is a fine-tuned version of Helsinki-NLP/opus-mt-ko-en on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.6544
- Bleu: 51.2387
- Gen Len: 11.5105
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
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Model tree for ding-diri-ding-dong/opus-mt-ko-en
Base model
Helsinki-NLP/opus-mt-ko-en