| --- |
| base_model: silmi224/finetune-led-35000 |
| tags: |
| - summarization |
| - generated_from_trainer |
| metrics: |
| - rouge |
| model-index: |
| - name: led-risalah_data_v17_2 |
| 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. --> |
|
|
| # led-risalah_data_v17_2 |
| |
| This model is a fine-tuned version of [silmi224/finetune-led-35000](https://huggingface.co/silmi224/finetune-led-35000) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 1.6183 |
| - Rouge1: 24.9438 |
| - Rouge2: 12.823 |
| - Rougel: 19.4874 |
| - Rougelsum: 23.9852 |
| |
| ## 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: 1e-05 |
| - train_batch_size: 1 |
| - eval_batch_size: 1 |
| - seed: 42 |
| - gradient_accumulation_steps: 4 |
| - total_train_batch_size: 4 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_steps: 400 |
| - num_epochs: 20 |
| - mixed_precision_training: Native AMP |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |
| |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:| |
| | 3.0463 | 1.0 | 20 | 2.6201 | 10.5455 | 3.3808 | 8.0147 | 9.7658 | |
| | 2.7524 | 2.0 | 40 | 2.3724 | 13.2211 | 4.2659 | 9.4319 | 11.4823 | |
| | 2.4437 | 3.0 | 60 | 2.1687 | 15.9732 | 4.6109 | 10.6226 | 14.1832 | |
| | 2.1607 | 4.0 | 80 | 2.0550 | 17.731 | 6.3571 | 10.6519 | 16.6744 | |
| | 2.0465 | 5.0 | 100 | 1.9641 | 19.3209 | 6.788 | 12.3334 | 17.2773 | |
| | 1.8932 | 6.0 | 120 | 1.8951 | 20.2099 | 9.1781 | 14.4373 | 18.5711 | |
| | 1.8485 | 7.0 | 140 | 1.8391 | 17.9081 | 7.2188 | 12.0437 | 16.1709 | |
| | 1.7211 | 8.0 | 160 | 1.7814 | 20.2991 | 8.2239 | 13.6757 | 18.9692 | |
| | 1.6461 | 9.0 | 180 | 1.7475 | 25.3547 | 10.5964 | 16.5484 | 23.7821 | |
| | 1.6109 | 10.0 | 200 | 1.7211 | 22.2062 | 9.3952 | 15.2277 | 21.1163 | |
| | 1.5818 | 11.0 | 220 | 1.7049 | 22.8022 | 9.2525 | 15.8587 | 21.4785 | |
| | 1.5194 | 12.0 | 240 | 1.6829 | 23.9497 | 11.1116 | 16.8015 | 22.8818 | |
| | 1.4541 | 13.0 | 260 | 1.6700 | 23.3403 | 11.4888 | 16.9861 | 22.4228 | |
| | 1.3816 | 14.0 | 280 | 1.6555 | 25.8179 | 13.2041 | 17.7017 | 24.7336 | |
| | 1.3908 | 15.0 | 300 | 1.6451 | 25.697 | 13.4504 | 18.41 | 24.7942 | |
| | 1.364 | 16.0 | 320 | 1.6224 | 25.7576 | 11.9706 | 17.695 | 24.2206 | |
| | 1.2521 | 17.0 | 340 | 1.6094 | 24.1556 | 12.942 | 18.5932 | 23.2197 | |
| | 1.2384 | 18.0 | 360 | 1.6041 | 25.1035 | 12.7288 | 18.2081 | 24.4216 | |
| | 1.2734 | 19.0 | 380 | 1.6075 | 25.482 | 13.4025 | 19.7018 | 25.1256 | |
| | 1.1228 | 20.0 | 400 | 1.6183 | 24.9438 | 12.823 | 19.4874 | 23.9852 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.41.2 |
| - Pytorch 2.1.2 |
| - Datasets 2.19.2 |
| - Tokenizers 0.19.1 |
| |
