Instructions to use yesj1234/mbart-mmt_mid2_ko-ja with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yesj1234/mbart-mmt_mid2_ko-ja with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("yesj1234/mbart-mmt_mid2_ko-ja") model = AutoModelForSeq2SeqLM.from_pretrained("yesj1234/mbart-mmt_mid2_ko-ja", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 2.73, | |
| "eval_bleu": 10.3643, | |
| "eval_gen_len": 17.4046, | |
| "eval_loss": 0.9049463272094727, | |
| "eval_runtime": 762.7452, | |
| "eval_samples": 13172, | |
| "eval_samples_per_second": 17.269, | |
| "eval_steps_per_second": 1.08, | |
| "predict_bleu": 13.1113, | |
| "predict_gen_len": 17.5101, | |
| "predict_loss": 0.9021268486976624, | |
| "predict_runtime": 763.1074, | |
| "predict_samples": 13173, | |
| "predict_samples_per_second": 17.262, | |
| "predict_steps_per_second": 1.08, | |
| "train_loss": 0.8521621839735243, | |
| "train_runtime": 30046.6476, | |
| "train_samples": 105385, | |
| "train_samples_per_second": 122.758, | |
| "train_steps_per_second": 7.673 | |
| } |