Model save

README.md

@@ -1,8 +1,10 @@
 ---
 license: apache-2.0
+base_model: NourFakih/Vit-GPT2-COCO2017Flickr-80k-08
 tags:
 - generated_from_trainer
-base_model: NourFakih/Vit-GPT2-COCO2017Flickr-40k-04
+metrics:
+- rouge
 model-index:
 - name: Vit-GPT2-COCO2017Flickr-80k-08
   results: []
@@ -13,19 +15,14 @@ should probably proofread and complete it, then remove this comment. -->
 
 # Vit-GPT2-COCO2017Flickr-80k-08
 
-This model is a fine-tuned version of [NourFakih/Vit-GPT2-COCO2017Flickr-40k-04](https://huggingface.co/NourFakih/Vit-GPT2-COCO2017Flickr-40k-04) on an unknown dataset.
+This model is a fine-tuned version of [NourFakih/Vit-GPT2-COCO2017Flickr-80k-08](https://huggingface.co/NourFakih/Vit-GPT2-COCO2017Flickr-80k-08) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- eval_loss: 0.4730
-- eval_rouge1: 39.8086
-- eval_rouge2: 14.7674
-- eval_rougeL: 36.1546
-- eval_rougeLsum: 36.1739
-- eval_gen_len: 11.7758
-- eval_runtime: 459.5392
-- eval_samples_per_second: 8.704
-- eval_steps_per_second: 2.176
-- epoch: 0.1
-- step: 500
+- Gen Len: 12.0243
+- Loss: 0.5354
+- Rouge1: 40.114
+- Rouge2: 14.6699
+- Rougel: 36.1001
+- Rougelsum: 36.1128
 
 ## Model description
 
@@ -54,9 +51,45 @@ The following hyperparameters were used during training:
 - lr_scheduler_type: linear
 - num_epochs: 3.0
 
+### Training results
+
+| Training Loss | Epoch | Step  | Gen Len | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum |
+|:-------------:|:-----:|:-----:|:-------:|:---------------:|:-------:|:-------:|:-------:|:---------:|
+| 0.3691        | 0.1   | 500   | 11.7758 | 0.4730          | 39.8086 | 14.7674 | 36.1546 | 36.1739   |
+| 0.3706        | 0.2   | 1000  | 11.5977 | 0.4739          | 39.8972 | 14.9064 | 36.1193 | 36.138    |
+| 0.3709        | 0.3   | 1500  | 11.7103 | 0.4759          | 39.9874 | 14.8528 | 36.3155 | 36.3317   |
+| 0.3721        | 0.4   | 2000  | 12.175  | 0.4678          | 39.7192 | 14.5844 | 35.8447 | 35.8728   |
+| 0.3655        | 0.5   | 2500  | 11.9002 | 0.4684          | 40.3132 | 15.1157 | 36.5749 | 36.5823   |
+| 0.3623        | 0.6   | 3000  | 12.025  | 0.4672          | 40.1643 | 14.978  | 36.3002 | 36.3232   |
+| 0.3676        | 0.7   | 3500  | 11.815  | 0.4623          | 40.5036 | 15.3751 | 36.8369 | 36.867    |
+| 0.3613        | 0.8   | 4000  | 12.054  | 0.4647          | 40.4078 | 15.3105 | 36.65   | 36.6732   |
+| 0.3539        | 0.9   | 4500  | 11.904  | 0.4634          | 40.3794 | 15.233  | 36.7155 | 36.7435   |
+| 0.3481        | 1.0   | 5000  | 11.738  | 0.4644          | 40.037  | 14.8477 | 36.3648 | 36.3903   |
+| 0.2889        | 1.1   | 5500  | 11.55   | 0.4897          | 40.1394 | 14.7595 | 36.4428 | 36.4696   |
+| 0.2908        | 1.2   | 6000  | 11.9823 | 0.4865          | 40.0479 | 14.8181 | 36.316  | 36.3519   |
+| 0.2882        | 1.3   | 6500  | 11.7945 | 0.4863          | 40.5912 | 15.3128 | 36.7638 | 36.7755   |
+| 0.2901        | 1.4   | 7000  | 11.87   | 0.4868          | 40.3138 | 14.9695 | 36.5032 | 36.5211   |
+| 0.2857        | 1.5   | 7500  | 11.776  | 0.4834          | 40.2242 | 14.9881 | 36.5381 | 36.5607   |
+| 0.279         | 1.6   | 8000  | 12.0132 | 0.4999          | 40.2751 | 15.0173 | 36.4172 | 36.4257   |
+| 0.281         | 1.7   | 8500  | 11.7685 | 0.4951          | 40.1172 | 14.8119 | 36.2966 | 36.296    |
+| 0.2831        | 1.8   | 9000  | 12.2293 | 0.4979          | 39.9913 | 14.7427 | 36.1539 | 36.1517   |
+| 0.2799        | 1.9   | 9500  | 11.8718 | 0.4911          | 40.5123 | 15.09   | 36.7528 | 36.7622   |
+| 0.2778        | 2.0   | 10000 | 12.0262 | 0.4929          | 40.5005 | 15.1027 | 36.6202 | 36.6327   |
+| 0.2318        | 2.1   | 10500 | 12.133  | 0.5237          | 40.1565 | 14.8022 | 36.1946 | 36.2074   |
+| 0.2279        | 2.2   | 11000 | 11.92   | 0.5278          | 40.5801 | 15.0843 | 36.7832 | 36.8021   |
+| 0.2272        | 2.3   | 11500 | 11.8057 | 0.5284          | 40.2332 | 14.8728 | 36.4401 | 36.4343   |
+| 0.2308        | 2.4   | 12000 | 11.9518 | 0.5263          | 39.9961 | 14.6475 | 36.035  | 36.0528   |
+| 0.2262        | 2.5   | 12500 | 11.9347 | 0.5322          | 40.3373 | 14.9137 | 36.3692 | 36.3718   |
+| 0.2233        | 2.6   | 13000 | 11.9147 | 0.5329          | 40.1924 | 14.776  | 36.1644 | 36.1593   |
+| 0.223         | 2.7   | 13500 | 11.9927 | 0.5370          | 40.3211 | 14.9563 | 36.3211 | 36.3345   |
+| 0.2241        | 2.8   | 14000 | 11.9367 | 0.5365          | 40.0897 | 14.6372 | 36.1484 | 36.1606   |
+| 0.2257        | 2.9   | 14500 | 12.0407 | 0.5332          | 40.2316 | 14.741  | 36.1795 | 36.1866   |
+| 0.2201        | 3.0   | 15000 | 12.0243 | 0.5354          | 40.114  | 14.6699 | 36.1001 | 36.1128   |
+
+
 ### Framework versions
 
-- Transformers 4.39.3
+- Transformers 4.41.2
 - Pytorch 2.1.2
-- Datasets 2.18.0
-- Tokenizers 0.15.2
+- Datasets 2.19.2
+- Tokenizers 0.19.1

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