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---
language:
- ko
- ja
base_model: facebook/mbart-large-50-many-to-many-mmt
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: mbart-mmt_mid3_ko-ja
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mbart-mmt_mid3_ko-ja
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8652
- Bleu: 10.1883
- Gen Len: 17.2057
## 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: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 35
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
| 1.6216 | 0.23 | 1500 | 1.5229 | 2.686 | 17.599 |
| 1.3587 | 0.46 | 3000 | 1.3061 | 4.0749 | 17.3772 |
| 1.2279 | 0.68 | 4500 | 1.1881 | 5.2878 | 17.3642 |
| 1.1408 | 0.91 | 6000 | 1.0994 | 5.4783 | 17.4093 |
| 0.9977 | 1.14 | 7500 | 1.0313 | 7.6015 | 17.36 |
| 0.9582 | 1.37 | 9000 | 0.9918 | 8.2303 | 17.3526 |
| 0.9525 | 1.59 | 10500 | 0.9811 | 8.2837 | 17.2597 |
| 0.9415 | 1.82 | 12000 | 0.9589 | 8.1592 | 17.2241 |
| 0.856 | 2.05 | 13500 | 0.9462 | 7.8401 | 17.4066 |
| 0.8273 | 2.28 | 15000 | 0.9336 | 8.6082 | 17.1918 |
| 0.8066 | 2.5 | 16500 | 0.9220 | 9.7751 | 17.5198 |
| 0.784 | 2.73 | 18000 | 0.8949 | 10.292 | 17.4097 |
| 0.8016 | 2.96 | 19500 | 0.8958 | 9.0262 | 17.4097 |
| 0.6872 | 3.19 | 21000 | 0.9043 | 9.7549 | 17.2672 |
| 0.7107 | 3.42 | 22500 | 0.8994 | 10.3016 | 17.0973 |
| 0.6726 | 3.64 | 24000 | 0.8747 | 10.5183 | 17.2871 |
| 0.6699 | 3.87 | 25500 | 0.8652 | 10.1883 | 17.2057 |
| 0.612 | 4.1 | 27000 | 0.8949 | 9.5697 | 17.2443 |
| 0.621 | 4.33 | 28500 | 0.8904 | 10.8592 | 17.329 |
| 0.6219 | 4.55 | 30000 | 0.8772 | 10.925 | 17.482 |
| 0.6164 | 4.78 | 31500 | 0.8694 | 11.8749 | 17.1624 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.5
- Tokenizers 0.14.1
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