---
language:
- ko
- ja
base_model: facebook/mbart-large-50-many-to-many-mmt
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: mbartLarge_koja_37p
  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. -->

# mbartLarge_koja_37p

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: 0.8995
- Bleu: 6.2934
- Gen Len: 17.4862

## 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: 1500
- num_epochs: 35

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Bleu   | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:|
| 1.2712        | 0.48  | 5500  | 1.2204          | 3.0858 | 18.1085 |
| 1.0946        | 0.97  | 11000 | 1.0578          | 3.3162 | 17.8651 |
| 0.9546        | 1.45  | 16500 | 0.9688          | 5.6024 | 17.902  |
| 0.89          | 1.94  | 22000 | 0.9414          | 5.1453 | 17.6144 |
| 0.834         | 2.42  | 27500 | 0.9213          | 5.3985 | 17.6899 |
| 0.7439        | 2.91  | 33000 | 0.8995          | 6.2934 | 17.4862 |
| 0.6803        | 3.39  | 38500 | 0.9016          | 6.3565 | 17.8899 |
| 0.733         | 3.88  | 44000 | 0.9226          | 7.0351 | 17.6112 |
| 0.6601        | 4.36  | 49500 | 0.9807          | 5.3084 | 17.4292 |
| 0.6933        | 4.84  | 55000 | 0.9238          | 6.8389 | 17.5131 |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1