---
base_model: ddobokki/vision-encoder-decoder-vit-gpt2-coco-ko
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: multimodla-Bitamin
  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. -->

# multimodla-Bitamin

This model is a fine-tuned version of [ddobokki/vision-encoder-decoder-vit-gpt2-coco-ko](https://huggingface.co/ddobokki/vision-encoder-decoder-vit-gpt2-coco-ko) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0055
- Rouge1: 11.8323
- Rouge2: 6.4292
- Rougel: 11.8967
- Rougelsum: 11.883
- Gen Len: 100.0

## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Rouge1  | Rouge2 | Rougel  | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 0.1504        | 1.0   | 2982  | 0.1027          | 0.2262  | 0.2262 | 0.2262  | 0.2262    | 100.0   |
| 0.0664        | 2.0   | 5964  | 0.0457          | 1.7181  | 1.2785 | 1.7523  | 1.7256    | 100.0   |
| 0.0281        | 3.0   | 8946  | 0.0185          | 5.3843  | 3.3178 | 5.4006  | 5.4032    | 100.0   |
| 0.0131        | 4.0   | 11928 | 0.0081          | 10.8352 | 5.9986 | 10.7776 | 10.8786   | 100.0   |
| 0.007         | 5.0   | 14910 | 0.0055          | 11.8323 | 6.4292 | 11.8967 | 11.883    | 100.0   |


### Framework versions

- Transformers 4.42.3
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1