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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
|