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--- |
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base_model: silmi224/finetune-led-35000 |
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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_v14 |
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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_v14 |
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This model is a fine-tuned version of [silmi224/finetune-led-35000](https://huggingface.co/silmi224/finetune-led-35000) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.7199 |
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- Rouge1 Precision: 0.6769 |
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- Rouge1 Recall: 0.1724 |
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- Rouge1 Fmeasure: 0.2744 |
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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: 2e-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: 4 |
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- total_train_batch_size: 4 |
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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: 10 |
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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 Precision | Rouge1 Recall | Rouge1 Fmeasure | |
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|:-------------:|:------:|:----:|:---------------:|:----------------:|:-------------:|:---------------:| |
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| 1.6706 | 0.9714 | 17 | 1.8400 | 0.6261 | 0.1547 | 0.2478 | |
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| 1.5177 | 2.0 | 35 | 1.7573 | 0.6586 | 0.1669 | 0.266 | |
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| 1.4016 | 2.9714 | 52 | 1.7266 | 0.6597 | 0.1689 | 0.2682 | |
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| 1.3182 | 4.0 | 70 | 1.7403 | 0.6564 | 0.1667 | 0.2653 | |
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| 1.217 | 4.9714 | 87 | 1.7272 | 0.657 | 0.1663 | 0.265 | |
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| 1.1559 | 6.0 | 105 | 1.7288 | 0.6493 | 0.1698 | 0.2687 | |
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| 1.1675 | 6.9714 | 122 | 1.7114 | 0.6727 | 0.1705 | 0.2717 | |
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| 1.1193 | 8.0 | 140 | 1.7118 | 0.6764 | 0.1734 | 0.2758 | |
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| 1.1101 | 8.9714 | 157 | 1.7232 | 0.6705 | 0.1726 | 0.2739 | |
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| 1.147 | 9.7143 | 170 | 1.7199 | 0.6769 | 0.1724 | 0.2744 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.1.2 |
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- Datasets 2.19.2 |
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- Tokenizers 0.19.1 |
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