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--- |
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license: apache-2.0 |
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base_model: NourFakih/image-captioning-Vit-GPT2-Flickr8k |
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tags: |
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- generated_from_trainer |
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metrics: |
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- rouge |
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model-index: |
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- name: Vit-GPT2-COCO2017Sample-Flickr8k |
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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-COCO2017Sample-Flickr8k |
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This model is a fine-tuned version of [NourFakih/image-captioning-Vit-GPT2-Flickr8k](https://huggingface.co/NourFakih/image-captioning-Vit-GPT2-Flickr8k) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2344 |
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- Rouge1: 41.2779 |
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- Rouge2: 15.8081 |
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- Rougel: 37.3177 |
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- Rougelsum: 37.2772 |
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- Gen Len: 11.568 |
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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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- 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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| 0.2485 | 0.08 | 500 | 11.382 | 0.2394 | 40.2445 | 13.9525 | 36.1329 | 36.1261 | |
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| 0.2336 | 0.16 | 1000 | 11.346 | 0.2376 | 39.9631 | 14.4981 | 36.1164 | 36.1265 | |
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| 0.2282 | 0.24 | 1500 | 11.07 | 0.2367 | 40.3107 | 14.533 | 36.489 | 36.5082 | |
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| 0.2249 | 0.32 | 2000 | 10.87 | 0.2338 | 41.0525 | 15.3076 | 37.0189 | 37.0365 | |
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| 0.2302 | 0.4 | 2500 | 11.31 | 0.2329 | 40.7052 | 14.8288 | 36.9272 | 36.9197 | |
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| 0.2255 | 0.48 | 3000 | 11.05 | 0.2321 | 40.6896 | 15.2723 | 36.9654 | 36.9515 | |
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| 0.2225 | 0.56 | 3500 | 10.946 | 0.2305 | 40.705 | 15.4878 | 37.0456 | 37.0235 | |
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| 0.2233 | 0.64 | 4000 | 11.25 | 0.2303 | 41.0229 | 15.179 | 37.081 | 37.0924 | |
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| 0.2177 | 0.72 | 4500 | 11.08 | 0.2307 | 40.0156 | 14.2972 | 36.0288 | 36.043 | |
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| 0.2159 | 0.8 | 5000 | 11.336 | 0.2298 | 40.4042 | 15.2531 | 36.6967 | 36.7003 | |
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| 0.2189 | 0.88 | 5500 | 11.39 | 0.2282 | 40.167 | 14.4847 | 36.3855 | 36.3742 | |
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| 0.2171 | 0.96 | 6000 | 11.002 | 0.2269 | 40.8528 | 15.1811 | 37.0586 | 37.0403 | |
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| 0.1962 | 1.04 | 6500 | 11.598 | 0.2296 | 40.6676 | 14.9888 | 36.7796 | 36.7703 | |
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| 0.1835 | 1.12 | 7000 | 11.022 | 0.2311 | 40.6188 | 15.2743 | 36.8519 | 36.8263 | |
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| 0.1835 | 1.2 | 7500 | 11.248 | 0.2289 | 40.6466 | 15.1727 | 36.6626 | 36.6427 | |
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| 0.1864 | 1.28 | 8000 | 11.408 | 0.2298 | 40.2408 | 15.0179 | 36.5594 | 36.5756 | |
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| 0.1838 | 1.36 | 8500 | 11.238 | 0.2295 | 41.0772 | 15.2152 | 37.0647 | 37.0648 | |
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| 0.1827 | 1.44 | 9000 | 11.28 | 0.2299 | 40.3263 | 14.9976 | 36.6444 | 36.6292 | |
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| 0.1828 | 1.52 | 9500 | 11.132 | 0.2299 | 40.9308 | 15.181 | 36.9028 | 36.8909 | |
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| 0.179 | 1.61 | 10000 | 11.164 | 0.2287 | 40.7406 | 15.2746 | 36.85 | 36.8748 | |
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| 0.1849 | 1.69 | 10500 | 10.988 | 0.2281 | 40.931 | 15.6479 | 37.0222 | 37.0071 | |
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| 0.1794 | 1.77 | 11000 | 11.218 | 0.2281 | 41.5198 | 15.9659 | 37.3709 | 37.386 | |
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| 0.1787 | 1.85 | 11500 | 11.274 | 0.2278 | 40.4006 | 14.9496 | 36.4608 | 36.4675 | |
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| 0.1798 | 1.93 | 12000 | 11.154 | 0.2279 | 41.3118 | 15.4673 | 37.4917 | 37.5101 | |
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| 0.1803 | 2.01 | 12500 | 11.23 | 0.2282 | 40.5652 | 15.1467 | 36.7946 | 36.7809 | |
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| 0.1519 | 2.09 | 13000 | 11.498 | 0.2361 | 40.8978 | 15.0865 | 36.7157 | 36.728 | |
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| 0.1515 | 2.17 | 13500 | 11.37 | 0.2360 | 40.9809 | 15.5877 | 37.0104 | 36.9942 | |
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| 0.1519 | 2.25 | 14000 | 11.504 | 0.2359 | 40.7947 | 15.3254 | 36.9574 | 36.9431 | |
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| 0.1543 | 2.33 | 14500 | 0.2346 | 40.7724 | 15.1837 | 36.9003 | 36.848 | 11.586 | |
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| 0.1548 | 2.41 | 15000 | 0.2355 | 40.7237 | 15.2394 | 37.0767 | 37.0405 | 11.294 | |
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| 0.1507 | 2.49 | 15500 | 0.2353 | 41.2661 | 15.7703 | 37.3669 | 37.32 | 11.308 | |
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| 0.1512 | 2.57 | 16000 | 0.2351 | 40.8777 | 15.2821 | 36.9591 | 36.9201 | 11.43 | |
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| 0.1525 | 2.65 | 16500 | 0.2350 | 40.6184 | 15.1824 | 36.655 | 36.6117 | 11.402 | |
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| 0.1522 | 2.73 | 17000 | 0.2343 | 41.2818 | 15.7174 | 37.3059 | 37.2695 | 11.502 | |
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| 0.1544 | 2.81 | 17500 | 0.2349 | 41.0821 | 15.5164 | 37.2206 | 37.1663 | 11.542 | |
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| 0.1498 | 2.89 | 18000 | 0.2346 | 41.2128 | 15.6698 | 37.2279 | 37.1874 | 11.582 | |
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| 0.1497 | 2.97 | 18500 | 0.2344 | 41.2779 | 15.8081 | 37.3177 | 37.2772 | 11.568 | |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 2.1.2 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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