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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_v4 |
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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_v4 |
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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: 1.8431 |
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- Rouge1: 16.5193 |
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- Rouge2: 8.3503 |
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- Rougel: 11.7271 |
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- Rougelsum: 15.6162 |
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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: 300 |
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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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| 3.3717 | 1.0 | 10 | 2.9094 | 8.8016 | 2.3126 | 6.2771 | 8.3716 | |
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| 3.3649 | 2.0 | 20 | 2.8898 | 9.2296 | 2.5864 | 6.5169 | 8.8408 | |
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| 3.3317 | 3.0 | 30 | 2.8578 | 9.4144 | 2.7476 | 6.7319 | 8.9607 | |
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| 3.2876 | 4.0 | 40 | 2.8156 | 9.2048 | 2.6478 | 6.8107 | 8.8212 | |
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| 3.2244 | 5.0 | 50 | 2.7651 | 7.4966 | 2.3382 | 5.9094 | 6.9392 | |
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| 3.1638 | 6.0 | 60 | 2.7088 | 8.8105 | 2.6633 | 6.809 | 8.3272 | |
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| 3.087 | 7.0 | 70 | 2.6486 | 9.3756 | 2.6957 | 7.2067 | 9.0197 | |
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| 3.0201 | 8.0 | 80 | 2.5859 | 9.5975 | 2.7885 | 6.9418 | 9.0329 | |
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| 2.9335 | 9.0 | 90 | 2.5224 | 9.5107 | 2.374 | 6.8494 | 8.9865 | |
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| 2.8603 | 10.0 | 100 | 2.4585 | 9.8073 | 2.8793 | 7.4445 | 9.4102 | |
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| 2.7774 | 11.0 | 110 | 2.3954 | 10.604 | 2.8025 | 7.8035 | 10.1927 | |
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| 2.7011 | 12.0 | 120 | 2.3347 | 10.3728 | 3.4421 | 7.8112 | 9.5918 | |
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| 2.634 | 13.0 | 130 | 2.2783 | 11.0596 | 3.3087 | 7.9686 | 10.047 | |
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| 2.5608 | 14.0 | 140 | 2.2253 | 12.4204 | 4.4276 | 8.5552 | 11.4364 | |
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| 2.4866 | 15.0 | 150 | 2.1782 | 12.8046 | 4.4267 | 8.8782 | 12.2253 | |
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| 2.4349 | 16.0 | 160 | 2.1369 | 13.0668 | 4.3763 | 8.7619 | 12.104 | |
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| 2.3851 | 17.0 | 170 | 2.1012 | 13.7679 | 4.6022 | 9.1874 | 12.7284 | |
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| 2.3302 | 18.0 | 180 | 2.0691 | 13.2512 | 4.6911 | 9.3187 | 11.8059 | |
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| 2.2836 | 19.0 | 190 | 2.0403 | 14.3491 | 5.7839 | 9.8346 | 13.3638 | |
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| 2.236 | 20.0 | 200 | 2.0150 | 13.9778 | 4.9493 | 9.5799 | 12.6063 | |
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| 2.1965 | 21.0 | 210 | 1.9910 | 14.0795 | 5.1926 | 9.3653 | 13.3801 | |
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| 2.1586 | 22.0 | 220 | 1.9704 | 14.1261 | 5.9801 | 9.7882 | 13.503 | |
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| 2.1325 | 23.0 | 230 | 1.9513 | 14.3575 | 6.0074 | 9.6053 | 13.672 | |
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| 2.099 | 24.0 | 240 | 1.9332 | 15.6132 | 6.3777 | 10.3533 | 14.9225 | |
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| 2.0703 | 25.0 | 250 | 1.9141 | 16.145 | 6.8437 | 10.6729 | 15.0299 | |
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| 2.0438 | 26.0 | 260 | 1.8984 | 15.3881 | 6.5977 | 10.048 | 14.7873 | |
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| 2.0187 | 27.0 | 270 | 1.8846 | 14.1595 | 6.3778 | 9.4685 | 13.3986 | |
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| 1.9954 | 28.0 | 280 | 1.8693 | 14.2631 | 6.3966 | 10.4774 | 13.4271 | |
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| 1.9723 | 29.0 | 290 | 1.8576 | 15.878 | 6.6511 | 10.8733 | 14.6417 | |
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| 1.9465 | 30.0 | 300 | 1.8431 | 16.5193 | 8.3503 | 11.7271 | 15.6162 | |
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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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