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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_v1 |
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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_v1 |
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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: 1.5716 |
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- Rouge1: 19.6052 |
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- Rouge2: 10.044 |
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- Rougel: 14.481 |
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- Rougelsum: 18.8794 |
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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-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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- lr_scheduler_warmup_steps: 150 |
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- num_epochs: 30 |
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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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| 3.3238 | 1.0 | 10 | 2.7939 | 8.2919 | 2.3936 | 6.4452 | 7.8858 | |
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| 3.0941 | 2.0 | 20 | 2.5393 | 8.884 | 2.3029 | 6.6763 | 8.2192 | |
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| 2.7749 | 3.0 | 30 | 2.2953 | 11.5346 | 3.61 | 7.9843 | 10.5072 | |
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| 2.5095 | 4.0 | 40 | 2.1271 | 13.3139 | 4.2267 | 9.3106 | 11.9731 | |
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| 2.3044 | 5.0 | 50 | 2.0140 | 14.8318 | 5.4652 | 10.4052 | 13.8404 | |
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| 2.1532 | 6.0 | 60 | 1.9280 | 15.6855 | 6.4587 | 10.7669 | 14.5093 | |
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| 2.032 | 7.0 | 70 | 1.8598 | 14.9367 | 5.7627 | 10.481 | 14.0571 | |
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| 1.9376 | 8.0 | 80 | 1.8049 | 14.8866 | 6.1106 | 10.0165 | 14.4686 | |
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| 1.8459 | 9.0 | 90 | 1.7491 | 13.6909 | 5.6398 | 9.2128 | 12.9399 | |
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| 1.7765 | 10.0 | 100 | 1.7213 | 16.7363 | 7.2146 | 11.2988 | 16.0402 | |
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| 1.704 | 11.0 | 110 | 1.6857 | 18.4687 | 8.7089 | 12.9138 | 17.8621 | |
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| 1.6542 | 12.0 | 120 | 1.6610 | 19.2238 | 8.9265 | 13.1614 | 17.6696 | |
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| 1.5957 | 13.0 | 130 | 1.6335 | 19.6057 | 9.8766 | 13.6908 | 18.6659 | |
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| 1.5413 | 14.0 | 140 | 1.6145 | 19.2875 | 9.7272 | 14.3241 | 17.7305 | |
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| 1.496 | 15.0 | 150 | 1.6232 | 18.1669 | 8.857 | 13.5735 | 17.1252 | |
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| 1.4535 | 16.0 | 160 | 1.6036 | 19.3501 | 10.1008 | 14.5871 | 18.4397 | |
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| 1.4204 | 17.0 | 170 | 1.5954 | 19.4201 | 10.3577 | 14.2019 | 18.4312 | |
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| 1.3829 | 18.0 | 180 | 1.5794 | 18.4944 | 9.4098 | 13.9 | 17.3891 | |
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| 1.3535 | 19.0 | 190 | 1.5814 | 19.9886 | 11.1416 | 15.0161 | 19.122 | |
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| 1.3328 | 20.0 | 200 | 1.5758 | 20.2011 | 10.5645 | 14.7218 | 19.1219 | |
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| 1.3063 | 21.0 | 210 | 1.5722 | 20.7308 | 10.834 | 15.3016 | 19.8805 | |
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| 1.2858 | 22.0 | 220 | 1.5745 | 19.648 | 10.77 | 14.0294 | 19.0395 | |
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| 1.2726 | 23.0 | 230 | 1.5651 | 20.4129 | 10.8196 | 15.0054 | 19.7253 | |
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| 1.2557 | 24.0 | 240 | 1.5709 | 18.6308 | 9.3525 | 13.8142 | 18.1621 | |
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| 1.2456 | 25.0 | 250 | 1.5659 | 19.6106 | 10.4499 | 14.4439 | 18.9271 | |
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| 1.233 | 26.0 | 260 | 1.5702 | 19.1583 | 9.7391 | 14.1738 | 18.5077 | |
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| 1.2267 | 27.0 | 270 | 1.5651 | 18.7654 | 9.8637 | 13.7809 | 18.2034 | |
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| 1.2203 | 28.0 | 280 | 1.5703 | 19.9698 | 10.4741 | 14.3559 | 19.389 | |
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| 1.2147 | 29.0 | 290 | 1.5739 | 19.9054 | 10.0052 | 14.6427 | 19.2278 | |
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| 1.2124 | 30.0 | 300 | 1.5716 | 19.6052 | 10.044 | 14.481 | 18.8794 | |
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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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