---
license: apache-2.0
tags:
- generated_from_trainer
base_model: NourFakih/Vit-GPT2-COCO2017Flickr-85k-09
metrics:
- rouge
model-index:
- name: Vit-GPT2-COCO2017Flickr-85k-09
  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-COCO2017Flickr-85k-09

This model is a fine-tuned version of [NourFakih/Vit-GPT2-COCO2017Flickr-85k-09](https://huggingface.co/NourFakih/Vit-GPT2-COCO2017Flickr-85k-09) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6343
- Rouge1: 38.8156
- Rouge2: 13.6737
- Rougel: 34.9479
- Rougelsum: 34.9604
- Gen Len: 12.1285

## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0

### Training results

| Training Loss | Epoch  | Step  | Gen Len | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum |
|:-------------:|:------:|:-----:|:-------:|:---------------:|:-------:|:-------:|:-------:|:---------:|
| 0.2429        | 0.0933 | 500   | 11.738  | 0.5351          | 39.4446 | 14.1599 | 35.6123 | 35.5846   |
| 0.2537        | 0.1866 | 1000  | 12.3488 | 0.5301          | 39.5332 | 14.4745 | 35.644  | 35.6159   |
| 0.2564        | 0.2799 | 1500  | 12.2455 | 0.5198          | 39.8297 | 14.555  | 35.8598 | 35.8344   |
| 0.2585        | 0.3732 | 2000  | 11.8575 | 0.5207          | 39.4558 | 14.0496 | 35.5597 | 35.526    |
| 0.2579        | 0.4665 | 2500  | 11.9733 | 0.5188          | 39.1359 | 14.125  | 35.4068 | 35.3709   |
| 0.2588        | 0.5599 | 3000  | 12.278  | 0.5196          | 39.0831 | 14.0658 | 35.4608 | 35.4283   |
| 0.2618        | 0.6532 | 3500  | 11.9942 | 0.5194          | 39.751  | 14.443  | 36.076  | 36.0475   |
| 0.2579        | 0.7465 | 4000  | 12.0512 | 0.5102          | 39.7601 | 14.5095 | 36.0252 | 35.9857   |
| 0.2569        | 0.8398 | 4500  | 11.6483 | 0.5199          | 39.398  | 13.8871 | 35.7218 | 35.6911   |
| 0.253         | 0.9331 | 5000  | 12.0198 | 0.5200          | 39.8951 | 14.4146 | 35.883  | 35.8507   |
| 0.2361        | 1.0264 | 5500  | 12.183  | 0.5605          | 39.3352 | 14.2234 | 35.3107 | 35.2772   |
| 0.2           | 1.1197 | 6000  | 11.8598 | 0.5702          | 39.2184 | 14.0096 | 35.5475 | 35.5042   |
| 0.2034        | 1.2130 | 6500  | 11.878  | 0.5543          | 39.7118 | 14.2757 | 35.7613 | 35.7316   |
| 0.1968        | 1.3063 | 7000  | 12.1725 | 0.5584          | 39.1847 | 13.9003 | 35.3962 | 35.3713   |
| 0.1986        | 1.3996 | 7500  | 11.8395 | 0.5572          | 39.4428 | 14.2672 | 35.7359 | 35.7093   |
| 0.1988        | 1.4930 | 8000  | 11.9932 | 0.5552          | 39.2719 | 14.0411 | 35.482  | 35.4833   |
| 0.1971        | 1.5864 | 8500  | 12.1003 | 0.5572          | 39.2681 | 14.1036 | 35.4466 | 35.4245   |
| 0.1978        | 1.6797 | 9000  | 12.1152 | 0.5667          | 39.2673 | 14.0918 | 35.4179 | 35.4169   |
| 0.1937        | 1.7730 | 9500  | 12.2208 | 0.5781          | 39.4115 | 14.1115 | 35.6952 | 35.6834   |
| 0.1897        | 1.8663 | 10000 | 11.8818 | 0.5754          | 39.2059 | 14.076  | 35.3392 | 35.3332   |
| 0.1898        | 1.9596 | 10500 | 11.8818 | 0.5720          | 39.4033 | 14.1447 | 35.598  | 35.5976   |
| 0.1685        | 2.0529 | 11000 | 12.0585 | 0.6186          | 38.4626 | 13.4695 | 34.7378 | 34.7294   |
| 0.1454        | 2.1462 | 11500 | 11.9448 | 0.6147          | 38.5335 | 13.5152 | 34.7075 | 34.7033   |
| 0.1434        | 2.2395 | 12000 | 12.1855 | 0.6229          | 39.0044 | 13.9276 | 35.2226 | 35.2116   |
| 0.1479        | 2.3328 | 12500 | 12.0273 | 0.6262          | 38.6281 | 13.5737 | 34.8247 | 34.8245   |
| 0.1452        | 2.4261 | 13000 | 12.0222 | 0.6243          | 38.9136 | 13.6727 | 35.0597 | 35.0643   |
| 0.1464        | 2.5195 | 13500 | 12.006  | 0.6309          | 38.9915 | 13.5041 | 34.9971 | 34.9991   |
| 0.1431        | 2.6128 | 14000 | 12.0602 | 0.6318          | 38.7595 | 13.5585 | 34.8308 | 34.834    |
| 0.1431        | 2.7061 | 14500 | 12.229  | 0.6277          | 38.8899 | 13.7343 | 34.9536 | 34.9513   |
| 0.1445        | 2.7995 | 15000 | 12.0343 | 0.6357          | 38.7681 | 13.5849 | 34.9764 | 34.9564   |
| 0.1379        | 2.8928 | 15500 | 0.6340  | 38.9196         | 13.6285 | 34.9761 | 34.9855 | 12.1242   |
| 0.1411        | 2.9861 | 16000 | 0.6343  | 38.8156         | 13.6737 | 34.9479 | 34.9604 | 12.1285   |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1