| --- |
| tags: |
| - summarization |
| - generated_from_trainer |
| model-index: |
| - name: led-risalah_data_v8 |
| 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_v8 |
|
|
| This model was trained from scratch on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 1.0169 |
| - Rouge1 Precision: 0.8329 |
| - Rouge1 Recall: 0.135 |
| - Rouge1 Fmeasure: 0.2293 |
|
|
| ## 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 | |
| |:-------------:|:-----:|:----:|:---------------:|:---------------:|:----------------:|:-------------:| |
| | 1.9061 | 1.0 | 15 | 1.9704 | 0.1528 | 0.5489 | 0.0894 | |
| | 1.8015 | 2.0 | 30 | 1.7979 | 0.2037 | 0.6934 | 0.1204 | |
| | 1.6484 | 3.0 | 45 | 1.7690 | 0.2107 | 0.72 | 0.1244 | |
| | 1.3656 | 4.0 | 60 | 1.7353 | 0.223 | 0.7526 | 0.1321 | |
| | 1.1833 | 5.0 | 75 | 1.7215 | 0.2172 | 0.7498 | 0.1283 | |
| | 1.1678 | 6.0 | 90 | 1.7365 | 0.2094 | 0.7063 | 0.1241 | |
| | 1.1258 | 7.0 | 105 | 1.7643 | 0.2193 | 0.7425 | 0.1299 | |
| | 1.0591 | 8.0 | 120 | 1.7697 | 0.2184 | 0.7328 | 0.1295 | |
| | 0.8896 | 9.0 | 135 | 1.7835 | 0.2207 | 0.7391 | 0.1306 | |
| | 1.0655 | 10.0 | 150 | 1.7985 | 0.2241 | 0.7559 | 0.1325 | |
| | 0.8386 | 11.0 | 165 | 1.8309 | 0.2217 | 0.7502 | 0.1314 | |
| | 0.8968 | 12.0 | 180 | 1.8377 | 0.2147 | 0.7179 | 0.1276 | |
| | 0.7863 | 13.0 | 195 | 1.8737 | 0.2172 | 0.7293 | 0.129 | |
| | 0.6942 | 14.0 | 210 | 1.8858 | 0.2185 | 0.7489 | 0.1291 | |
| | 0.6656 | 15.0 | 225 | 1.9181 | 0.2243 | 0.7566 | 0.1328 | |
| | 0.6672 | 16.0 | 240 | 1.9407 | 0.2224 | 0.7513 | 0.1315 | |
| | 0.6405 | 17.0 | 255 | 1.9416 | 0.2151 | 0.7369 | 0.1272 | |
| | 0.7382 | 18.0 | 270 | 1.9533 | 0.2214 | 0.7506 | 0.1311 | |
| | 0.6445 | 19.0 | 285 | 1.9605 | 0.2136 | 0.7292 | 0.1262 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.41.2 |
| - Pytorch 2.1.2 |
| - Datasets 2.19.2 |
| - Tokenizers 0.19.1 |
| |
