kd4_opus-mt-ko-en

kd4_opus-mt-ko-en

This model is a fine-tuned version of Helsinki-NLP/opus-mt-ko-en on the kde4 dataset. It achieves the following results on the evaluation set:

• Loss: 1.3924
• Bleu: 32.1162

See translation_ko_en.ipynb

Model description

More information needed

Intended uses & limitations

More information needed

Usage

You can use this model directly with a pipeline for translation language modeling:

>>> from transformers import pipeline
>>> translator = pipeline('translation',model='chunwoolee0/kd4_opus-mt-ko-e')
>>> translator("점심 식사 후에 산책가자.")

[{'translation_text': "Let's go for a walk after noon."}]

>>> translator("이 강좌는 허깅페이스가 만든 거야.")
[{'translation_text': 'This is a course by Huggingspace.'}]

>>> translator("오늘은 늦게 일어났다.")
[{'translation_text': "I'm up late today."}]

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: 32
• eval_batch_size: 64
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 3

Training results

Step Training Loss 500 1.858500 1000 1.781400 1500 1.715200 2000 1.678100 2500 1.546600 3000 1.488700 3500 1.503500 4000 1.455100 4500 1.419100 5000 1.393400 5500 1.357100 6000 1.339400 TrainOutput(global_step=6474, training_loss=1.532715692246148, metrics={'train_runtime': 1035.7775, 'train_samples_per_second': 199.957, 'train_steps_per_second': 6.25, 'total_flos': 2551308264603648.0, 'train_loss': 1.532715692246148, 'epoch': 3.0})

Framework versions

• Transformers 4.31.0
• Pytorch 2.0.1+cu118
• Datasets 2.14.4
• Tokenizers 0.13.3