| --- |
| tags: |
| - summarization |
| - generated_from_trainer |
| model-index: |
| - name: led-risalah_data_v2 |
| 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_v2 |
|
|
| This model was trained from scratch on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.7850 |
| - Rouge1 Precision: 0.816 |
| - Rouge1 Recall: 0.2149 |
| - Rouge1 Fmeasure: 0.3393 |
|
|
| ## 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: 1 |
| - eval_batch_size: 1 |
| - seed: 42 |
| - gradient_accumulation_steps: 8 |
| - total_train_batch_size: 8 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 20 |
| - mixed_precision_training: Native AMP |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Rouge1 Fmeasure | Rouge1 Precision | Rouge1 Recall | |
| |:-------------:|:-------:|:----:|:---------------:|:---------------:|:----------------:|:-------------:| |
| | 2.4163 | 0.9143 | 8 | 1.9482 | 0.2001 | 0.4982 | 0.1254 | |
| | 1.6578 | 1.9429 | 17 | 1.8076 | 0.2489 | 0.6295 | 0.1554 | |
| | 1.656 | 2.9143 | 24 | 1.4664 | 0.2459 | 0.6118 | 0.154 | |
| | 1.5142 | 3.9429 | 33 | 1.4191 | 0.2546 | 0.646 | 0.159 | |
| | 1.4169 | 4.9714 | 42 | 1.4162 | 0.27 | 0.6675 | 0.1698 | |
| | 1.4123 | 6.9143 | 56 | 1.3197 | 0.2807 | 0.7054 | 0.1755 | |
| | 1.3398 | 7.9429 | 65 | 1.3156 | 0.2797 | 0.6912 | 0.1759 | |
| | 1.146 | 8.9714 | 74 | 1.3247 | 0.2925 | 0.728 | 0.1834 | |
| | 1.1481 | 10.0 | 83 | 1.3366 | 0.2739 | 0.6799 | 0.1718 | |
| | 1.2033 | 10.9143 | 91 | 1.3387 | 0.2789 | 0.69 | 0.1752 | |
| | 1.0855 | 11.9429 | 100 | 1.3375 | 0.2888 | 0.7146 | 0.1814 | |
| | 0.999 | 12.9714 | 109 | 1.3589 | 0.2922 | 0.7265 | 0.1831 | |
| | 1.0034 | 14.0 | 118 | 1.3601 | 0.2872 | 0.7157 | 0.1801 | |
| | 0.9831 | 14.9143 | 126 | 1.3762 | 0.2851 | 0.7024 | 0.1792 | |
| | 0.9347 | 15.9429 | 135 | 1.3743 | 0.2769 | 0.6841 | 0.174 | |
| | 0.9018 | 16.9714 | 144 | 1.3820 | 0.2862 | 0.7139 | 0.1797 | |
| | 0.8939 | 18.0 | 153 | 1.3841 | 0.2879 | 0.7134 | 0.1806 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.42.3 |
| - Pytorch 2.3.0+cu121 |
| - Datasets 2.20.0 |
| - Tokenizers 0.19.1 |
| |
