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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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base_model: NourFakih/Vit-GPT2-COCO2017Flickr-85k-09 |
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metrics: |
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- rouge |
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model-index: |
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- name: Vit-GPT2-COCO2017Flickr-85k-09 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# Vit-GPT2-COCO2017Flickr-85k-09 |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6343 |
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- Rouge1: 38.8156 |
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- Rouge2: 13.6737 |
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- Rougel: 34.9479 |
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- Rougelsum: 34.9604 |
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- Gen Len: 12.1285 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 16 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 3.0 |
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### Training results |
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| Training Loss | Epoch | Step | Gen Len | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |
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|:-------------:|:------:|:-----:|:-------:|:---------------:|:-------:|:-------:|:-------:|:---------:| |
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| 0.2429 | 0.0933 | 500 | 11.738 | 0.5351 | 39.4446 | 14.1599 | 35.6123 | 35.5846 | |
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| 0.2537 | 0.1866 | 1000 | 12.3488 | 0.5301 | 39.5332 | 14.4745 | 35.644 | 35.6159 | |
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| 0.2564 | 0.2799 | 1500 | 12.2455 | 0.5198 | 39.8297 | 14.555 | 35.8598 | 35.8344 | |
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| 0.2585 | 0.3732 | 2000 | 11.8575 | 0.5207 | 39.4558 | 14.0496 | 35.5597 | 35.526 | |
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| 0.2579 | 0.4665 | 2500 | 11.9733 | 0.5188 | 39.1359 | 14.125 | 35.4068 | 35.3709 | |
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| 0.2588 | 0.5599 | 3000 | 12.278 | 0.5196 | 39.0831 | 14.0658 | 35.4608 | 35.4283 | |
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| 0.2618 | 0.6532 | 3500 | 11.9942 | 0.5194 | 39.751 | 14.443 | 36.076 | 36.0475 | |
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| 0.2579 | 0.7465 | 4000 | 12.0512 | 0.5102 | 39.7601 | 14.5095 | 36.0252 | 35.9857 | |
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| 0.2569 | 0.8398 | 4500 | 11.6483 | 0.5199 | 39.398 | 13.8871 | 35.7218 | 35.6911 | |
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| 0.253 | 0.9331 | 5000 | 12.0198 | 0.5200 | 39.8951 | 14.4146 | 35.883 | 35.8507 | |
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| 0.2361 | 1.0264 | 5500 | 12.183 | 0.5605 | 39.3352 | 14.2234 | 35.3107 | 35.2772 | |
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| 0.2 | 1.1197 | 6000 | 11.8598 | 0.5702 | 39.2184 | 14.0096 | 35.5475 | 35.5042 | |
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| 0.2034 | 1.2130 | 6500 | 11.878 | 0.5543 | 39.7118 | 14.2757 | 35.7613 | 35.7316 | |
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| 0.1968 | 1.3063 | 7000 | 12.1725 | 0.5584 | 39.1847 | 13.9003 | 35.3962 | 35.3713 | |
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| 0.1986 | 1.3996 | 7500 | 11.8395 | 0.5572 | 39.4428 | 14.2672 | 35.7359 | 35.7093 | |
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| 0.1988 | 1.4930 | 8000 | 11.9932 | 0.5552 | 39.2719 | 14.0411 | 35.482 | 35.4833 | |
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| 0.1971 | 1.5864 | 8500 | 12.1003 | 0.5572 | 39.2681 | 14.1036 | 35.4466 | 35.4245 | |
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| 0.1978 | 1.6797 | 9000 | 12.1152 | 0.5667 | 39.2673 | 14.0918 | 35.4179 | 35.4169 | |
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| 0.1937 | 1.7730 | 9500 | 12.2208 | 0.5781 | 39.4115 | 14.1115 | 35.6952 | 35.6834 | |
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| 0.1897 | 1.8663 | 10000 | 11.8818 | 0.5754 | 39.2059 | 14.076 | 35.3392 | 35.3332 | |
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| 0.1898 | 1.9596 | 10500 | 11.8818 | 0.5720 | 39.4033 | 14.1447 | 35.598 | 35.5976 | |
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| 0.1685 | 2.0529 | 11000 | 12.0585 | 0.6186 | 38.4626 | 13.4695 | 34.7378 | 34.7294 | |
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| 0.1454 | 2.1462 | 11500 | 11.9448 | 0.6147 | 38.5335 | 13.5152 | 34.7075 | 34.7033 | |
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| 0.1434 | 2.2395 | 12000 | 12.1855 | 0.6229 | 39.0044 | 13.9276 | 35.2226 | 35.2116 | |
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| 0.1479 | 2.3328 | 12500 | 12.0273 | 0.6262 | 38.6281 | 13.5737 | 34.8247 | 34.8245 | |
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| 0.1452 | 2.4261 | 13000 | 12.0222 | 0.6243 | 38.9136 | 13.6727 | 35.0597 | 35.0643 | |
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| 0.1464 | 2.5195 | 13500 | 12.006 | 0.6309 | 38.9915 | 13.5041 | 34.9971 | 34.9991 | |
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| 0.1431 | 2.6128 | 14000 | 12.0602 | 0.6318 | 38.7595 | 13.5585 | 34.8308 | 34.834 | |
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| 0.1431 | 2.7061 | 14500 | 12.229 | 0.6277 | 38.8899 | 13.7343 | 34.9536 | 34.9513 | |
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| 0.1445 | 2.7995 | 15000 | 12.0343 | 0.6357 | 38.7681 | 13.5849 | 34.9764 | 34.9564 | |
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| 0.1379 | 2.8928 | 15500 | 0.6340 | 38.9196 | 13.6285 | 34.9761 | 34.9855 | 12.1242 | |
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| 0.1411 | 2.9861 | 16000 | 0.6343 | 38.8156 | 13.6737 | 34.9479 | 34.9604 | 12.1285 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.1.2 |
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- Datasets 2.19.2 |
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- Tokenizers 0.19.1 |
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