transformers pytorch mbart text2text-generation generated_from_trainer ko en base_model:facebook/mbart-large-50-many-to-many-mmt autotrain_compatible endpoints_compatible region:us
---
language:
- ko
- en
base_model: facebook/mbart-large-50-many-to-many-mmt
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: ko-en_mbartLarge_exp5p_linear
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# ko-en_mbartLarge_exp5p_linear

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: 1.2308
- Bleu: 26.2764
- Gen Len: 18.3888

## 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: 40

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Bleu    | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
| 1.7665        | 0.46  | 1000  | 1.6564          | 17.6773 | 18.7196 |
| 1.5688        | 0.93  | 2000  | 1.4939          | 20.8837 | 18.3983 |
| 1.457         | 1.39  | 3000  | 1.4350          | 21.9168 | 18.458  |
| 1.4107        | 1.86  | 4000  | 1.3752          | 22.8881 | 18.4826 |
| 1.3039        | 2.32  | 5000  | 1.3327          | 23.8115 | 18.4348 |
| 1.282         | 2.78  | 6000  | 1.3079          | 24.235  | 18.3561 |
| 1.2133        | 3.25  | 7000  | 1.2820          | 24.8877 | 18.5204 |
| 1.1787        | 3.71  | 8000  | 1.2580          | 25.2719 | 18.415  |
| 1.1154        | 4.18  | 9000  | 1.2543          | 25.5507 | 18.3528 |
| 1.0956        | 4.64  | 10000 | 1.2415          | 25.7284 | 18.5348 |
| 1.023         | 5.1   | 11000 | 1.2410          | 25.7912 | 18.3347 |
| 0.95          | 5.57  | 12000 | 1.2327          | 25.9921 | 18.2593 |
| 0.9476        | 6.03  | 13000 | 1.2631          | 25.829  | 18.3686 |
| 0.9061        | 6.5   | 14000 | 1.2548          | 25.8316 | 18.7481 |
| 0.9037        | 6.96  | 15000 | 1.2308          | 26.2764 | 18.3888 |
| 0.7431        | 7.42  | 16000 | 1.2716          | 25.9268 | 18.256  |
| 0.7526        | 7.89  | 17000 | 1.2655          | 25.9883 | 18.2052 |
| 0.6654        | 8.35  | 18000 | 1.3118          | 25.6866 | 18.2217 |
| 0.6953        | 8.82  | 19000 | 1.3050          | 25.8958 | 18.3387 |


### Framework versions

- Transformers 4.34.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.5
- Tokenizers 0.14.1
