| --- |
| base_model: silmi224/finetune-led-35000 |
| tags: |
| - summarization |
| - generated_from_trainer |
| model-index: |
| - name: led-risalah_data_v14 |
| 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_v14 |
|
|
| 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: 1.7199 |
| - Rouge1 Precision: 0.6769 |
| - Rouge1 Recall: 0.1724 |
| - Rouge1 Fmeasure: 0.2744 |
|
|
| ## 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: 2e-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 |
| - num_epochs: 10 |
| - mixed_precision_training: Native AMP |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Rouge1 Precision | Rouge1 Recall | Rouge1 Fmeasure | |
| |:-------------:|:------:|:----:|:---------------:|:----------------:|:-------------:|:---------------:| |
| | 1.6706 | 0.9714 | 17 | 1.8400 | 0.6261 | 0.1547 | 0.2478 | |
| | 1.5177 | 2.0 | 35 | 1.7573 | 0.6586 | 0.1669 | 0.266 | |
| | 1.4016 | 2.9714 | 52 | 1.7266 | 0.6597 | 0.1689 | 0.2682 | |
| | 1.3182 | 4.0 | 70 | 1.7403 | 0.6564 | 0.1667 | 0.2653 | |
| | 1.217 | 4.9714 | 87 | 1.7272 | 0.657 | 0.1663 | 0.265 | |
| | 1.1559 | 6.0 | 105 | 1.7288 | 0.6493 | 0.1698 | 0.2687 | |
| | 1.1675 | 6.9714 | 122 | 1.7114 | 0.6727 | 0.1705 | 0.2717 | |
| | 1.1193 | 8.0 | 140 | 1.7118 | 0.6764 | 0.1734 | 0.2758 | |
| | 1.1101 | 8.9714 | 157 | 1.7232 | 0.6705 | 0.1726 | 0.2739 | |
| | 1.147 | 9.7143 | 170 | 1.7199 | 0.6769 | 0.1724 | 0.2744 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.41.2 |
| - Pytorch 2.1.2 |
| - Datasets 2.19.2 |
| - Tokenizers 0.19.1 |
| |
