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--- |
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tags: |
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- summarization |
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- generated_from_trainer |
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model-index: |
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- name: led-risalah_data_v2 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# led-risalah_data_v2 |
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This model was trained from scratch on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.6785 |
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- Rouge1 Precision: 0.6665 |
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- Rouge1 Recall: 0.1816 |
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- Rouge1 Fmeasure: 0.284 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 1 |
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- eval_batch_size: 1 |
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- seed: 42 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 8 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 20 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 Fmeasure | Rouge1 Precision | Rouge1 Recall | |
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|:-------------:|:-----:|:----:|:---------------:|:---------------:|:----------------:|:-------------:| |
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| 2.4597 | 0.91 | 8 | 1.8034 | 0.1951 | 0.4699 | 0.1246 | |
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| 1.7706 | 1.94 | 17 | 1.6403 | 0.2451 | 0.6043 | 0.1554 | |
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| 1.5072 | 2.97 | 26 | 1.5947 | 0.2628 | 0.6236 | 0.1676 | |
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| 1.4018 | 4.0 | 35 | 1.5688 | 0.2789 | 0.656 | 0.1782 | |
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| 1.2761 | 4.91 | 43 | 1.5454 | 0.2723 | 0.6434 | 0.1736 | |
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| 1.1779 | 5.94 | 52 | 1.5636 | 0.2889 | 0.6794 | 0.1843 | |
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| 1.1235 | 6.97 | 61 | 1.5430 | 0.2965 | 0.6913 | 0.1902 | |
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| 1.0529 | 8.0 | 70 | 1.5639 | 0.2829 | 0.6705 | 0.1805 | |
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| 0.9883 | 8.91 | 78 | 1.5740 | 0.2817 | 0.6757 | 0.1798 | |
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| 0.9274 | 9.94 | 87 | 1.5793 | 0.2771 | 0.6623 | 0.1764 | |
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| 0.925 | 10.97 | 96 | 1.6072 | 0.2821 | 0.665 | 0.18 | |
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| 0.858 | 12.0 | 105 | 1.6129 | 0.284 | 0.6625 | 0.1817 | |
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| 0.8182 | 12.91 | 113 | 1.6396 | 0.2765 | 0.6567 | 0.1761 | |
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| 0.7974 | 13.94 | 122 | 1.6445 | 0.2759 | 0.659 | 0.1759 | |
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| 0.7524 | 14.97 | 131 | 1.6585 | 0.2763 | 0.6585 | 0.1759 | |
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| 0.7743 | 16.0 | 140 | 1.6779 | 0.2788 | 0.6594 | 0.1779 | |
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| 0.7486 | 16.91 | 148 | 1.6742 | 0.2851 | 0.6666 | 0.1819 | |
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| 0.676 | 17.94 | 157 | 1.6790 | 0.2859 | 0.6707 | 0.1827 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.1+cu121 |
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- Datasets 2.14.5 |
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- Tokenizers 0.15.1 |
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