---
library_name: transformers
base_model: motheecreator/ViT-GPT2-Image_Captioning_model
tags:
- generated_from_trainer
- image-to-text
metrics:
- bleu
model-index:
- name: ViT-GPT2
  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. -->

# ViT-GPT2

This model is a fine-tuned version of [motheecreator/ViT-GPT2-Image_Captioning_model](https://huggingface.co/motheecreator/ViT-GPT2-Image_Captioning_model) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.125337
- Rouge2 Precision: None
- Rouge2 Recall: None
- Rouge2 Fmeasure: 0.155
- Bleu: 9.7054

## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | Bleu   |
|:-------------:|:------:|:----:|:---------------:|:----------------:|:-------------:|:---------------:|:------:|
| 2.1537        | 0.9993 | 1171 | 2.13666          | None             | None          | 0.1531          | 9.4673 |
| 2.0434        | 1.9985 | 2342 | 2.125337          | None             | None          | 0.155          | 9.7054 |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.0
- Datasets 3.0.0
- Tokenizers 0.19.1