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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_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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# exp2-led-risalah_data_v2 |
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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: 1.6223 |
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- Rouge1: 20.4859 |
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- Rouge2: 10.2651 |
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- Rougel: 14.7662 |
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- Rougelsum: 19.2553 |
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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: 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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| 3.3339 | 1.0 | 10 | 2.8010 | 8.3493 | 2.4084 | 6.4284 | 7.9202 | |
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| 3.1015 | 2.0 | 20 | 2.5436 | 8.9461 | 2.3615 | 6.7822 | 8.3767 | |
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| 2.779 | 3.0 | 30 | 2.2976 | 11.5444 | 3.5251 | 8.0258 | 10.4456 | |
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| 2.5118 | 4.0 | 40 | 2.1282 | 13.3666 | 4.1766 | 9.2522 | 11.9858 | |
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| 2.3057 | 5.0 | 50 | 2.0147 | 15.021 | 5.5582 | 10.3573 | 14.1171 | |
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| 2.1541 | 6.0 | 60 | 1.9283 | 15.937 | 6.8169 | 11.0627 | 14.6866 | |
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| 2.0326 | 7.0 | 70 | 1.8601 | 14.7364 | 5.5533 | 10.3599 | 13.9586 | |
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| 1.938 | 8.0 | 80 | 1.8050 | 14.8895 | 6.0535 | 9.9969 | 14.4782 | |
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| 1.8462 | 9.0 | 90 | 1.7492 | 14.0282 | 5.8353 | 9.232 | 13.2213 | |
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| 1.7767 | 10.0 | 100 | 1.7214 | 16.7779 | 7.2314 | 11.1359 | 16.1369 | |
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| 1.7042 | 11.0 | 110 | 1.6857 | 18.4084 | 8.7509 | 12.7906 | 17.8835 | |
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| 1.6543 | 12.0 | 120 | 1.6610 | 19.2909 | 8.9371 | 13.1256 | 17.6865 | |
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| 1.5958 | 13.0 | 130 | 1.6335 | 19.8664 | 9.7174 | 13.6907 | 18.8411 | |
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| 1.5414 | 14.0 | 140 | 1.6145 | 19.2112 | 9.6741 | 14.1273 | 17.7185 | |
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| 1.496 | 15.0 | 150 | 1.6234 | 18.8087 | 9.0827 | 13.6381 | 17.6146 | |
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| 1.4534 | 16.0 | 160 | 1.6035 | 19.4539 | 10.135 | 14.4283 | 18.5099 | |
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| 1.4177 | 17.0 | 170 | 1.5948 | 19.6367 | 10.405 | 14.0816 | 18.0333 | |
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| 1.3742 | 18.0 | 180 | 1.5712 | 18.8434 | 10.1431 | 13.7222 | 17.6519 | |
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| 1.3378 | 19.0 | 190 | 1.5829 | 18.9662 | 10.7079 | 13.9422 | 18.1457 | |
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| 1.3068 | 20.0 | 200 | 1.5746 | 20.724 | 11.3974 | 15.1529 | 19.8343 | |
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| 1.2669 | 21.0 | 210 | 1.5476 | 19.0993 | 9.6869 | 13.815 | 18.5096 | |
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| 1.2315 | 22.0 | 220 | 1.5606 | 20.4637 | 10.7418 | 14.634 | 19.5588 | |
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| 1.2005 | 23.0 | 230 | 1.5617 | 19.3271 | 9.8272 | 14.2547 | 18.5378 | |
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| 1.1649 | 24.0 | 240 | 1.5618 | 20.3699 | 11.3093 | 14.2115 | 19.4149 | |
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| 1.1344 | 25.0 | 250 | 1.5649 | 20.8124 | 11.3997 | 15.8717 | 20.0457 | |
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| 1.099 | 26.0 | 260 | 1.5985 | 19.8977 | 9.9926 | 14.1038 | 19.0059 | |
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| 1.065 | 27.0 | 270 | 1.5678 | 20.7049 | 10.9546 | 14.4462 | 19.5927 | |
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| 1.0344 | 28.0 | 280 | 1.6225 | 21.3939 | 11.2821 | 15.0261 | 20.3781 | |
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| 1.0029 | 29.0 | 290 | 1.5831 | 20.7287 | 11.0327 | 14.3893 | 19.9485 | |
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| 0.9711 | 30.0 | 300 | 1.6223 | 20.4859 | 10.2651 | 14.7662 | 19.2553 | |
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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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