| --- |
| base_model: silmi224/finetune-led-35000 |
| tags: |
| - summarization |
| - generated_from_trainer |
| model-index: |
| - name: led-risalah_data_v4.1 |
| 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_v4.1 |
|
|
| 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: 1.6280 |
| - Rouge1 Precision: 0.6744 |
| - Rouge1 Recall: 0.1777 |
| - Rouge1 Fmeasure: 0.2803 |
|
|
| ## 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: 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 | |
| |:-------------:|:------:|:----:|:---------------:|:----------------:|:-------------:|:---------------:| |
| | 2.3322 | 0.9714 | 17 | 1.7433 | 0.6164 | 0.1561 | 0.2485 | |
| | 1.5523 | 2.0 | 35 | 1.6294 | 0.6379 | 0.1588 | 0.2536 | |
| | 1.3282 | 2.9714 | 52 | 1.6077 | 0.6252 | 0.1561 | 0.2491 | |
| | 1.2231 | 4.0 | 70 | 1.5914 | 0.6599 | 0.1704 | 0.2706 | |
| | 1.1213 | 4.9714 | 87 | 1.6090 | 0.6771 | 0.1707 | 0.272 | |
| | 1.0491 | 6.0 | 105 | 1.6135 | 0.6656 | 0.17 | 0.2704 | |
| | 0.9272 | 6.9714 | 122 | 1.6004 | 0.6758 | 0.1755 | 0.2783 | |
| | 0.8667 | 8.0 | 140 | 1.6217 | 0.7048 | 0.1826 | 0.2896 | |
| | 0.884 | 8.9714 | 157 | 1.6185 | 0.7143 | 0.1856 | 0.294 | |
| | 0.8415 | 9.7143 | 170 | 1.6280 | 0.6744 | 0.1777 | 0.2803 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.41.2 |
| - Pytorch 2.1.2 |
| - Datasets 2.19.2 |
| - Tokenizers 0.19.1 |
| |
