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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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metrics: |
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- rouge |
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model-index: |
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- name: exp2-led-risalah_data_v3 |
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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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# exp2-led-risalah_data_v3 |
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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: 1.9287 |
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- Rouge1: 16.3563 |
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- Rouge2: 6.3361 |
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- Rougel: 10.2361 |
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- Rougelsum: 15.4499 |
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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: 1e-06 |
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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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- lr_scheduler_warmup_steps: 150 |
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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 | Rouge2 | Rougel | Rougelsum | |
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|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:| |
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| 3.3696 | 1.0 | 10 | 2.9032 | 8.9827 | 2.4864 | 6.5741 | 8.374 | |
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| 3.3479 | 2.0 | 20 | 2.8646 | 9.4368 | 2.6548 | 6.6897 | 8.9073 | |
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| 3.2858 | 3.0 | 30 | 2.8050 | 8.3204 | 2.4233 | 6.4571 | 7.8334 | |
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| 3.204 | 4.0 | 40 | 2.7299 | 7.9763 | 2.7995 | 6.1867 | 7.5793 | |
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| 3.0987 | 5.0 | 50 | 2.6458 | 9.4672 | 2.877 | 7.2221 | 8.8929 | |
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| 2.9964 | 6.0 | 60 | 2.5576 | 9.3123 | 2.635 | 6.8591 | 8.8136 | |
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| 2.8831 | 7.0 | 70 | 2.4682 | 9.8347 | 2.8621 | 7.3463 | 9.346 | |
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| 2.7834 | 8.0 | 80 | 2.3818 | 9.756 | 2.6064 | 7.3736 | 9.0638 | |
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| 2.6712 | 9.0 | 90 | 2.3005 | 10.6798 | 3.5515 | 7.9318 | 9.5388 | |
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| 2.5781 | 10.0 | 100 | 2.2261 | 11.4114 | 3.5141 | 8.0732 | 10.6929 | |
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| 2.4807 | 11.0 | 110 | 2.1623 | 12.9396 | 4.3079 | 9.1668 | 11.7355 | |
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| 2.403 | 12.0 | 120 | 2.1101 | 13.27 | 4.7477 | 9.0288 | 12.277 | |
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| 2.3358 | 13.0 | 130 | 2.0644 | 15.1784 | 5.3452 | 10.1318 | 13.8506 | |
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| 2.2701 | 14.0 | 140 | 2.0249 | 14.1959 | 5.2981 | 10.2128 | 12.8727 | |
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| 2.2032 | 15.0 | 150 | 1.9925 | 14.4716 | 5.5627 | 9.58 | 13.7089 | |
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| 2.1608 | 16.0 | 160 | 1.9685 | 14.2815 | 5.9009 | 9.516 | 13.4755 | |
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| 2.1338 | 17.0 | 170 | 1.9509 | 15.6523 | 6.3449 | 10.2105 | 14.9489 | |
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| 2.104 | 18.0 | 180 | 1.9383 | 16.3987 | 7.0987 | 10.8261 | 15.8296 | |
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| 2.0896 | 19.0 | 190 | 1.9308 | 16.0883 | 6.3808 | 10.0722 | 15.17 | |
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| 2.0758 | 20.0 | 200 | 1.9287 | 16.3563 | 6.3361 | 10.2361 | 15.4499 | |
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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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