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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: led-risalah_data_v17_3 |
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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_v17_3 |
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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: 1.6551 |
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- Rouge1: 25.33 |
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- Rouge2: 12.4758 |
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- Rougel: 18.3801 |
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- Rougelsum: 24.0275 |
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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: 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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- lr_scheduler_warmup_steps: 200 |
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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 | Rouge2 | Rougel | Rougelsum | |
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|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:| |
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| 2.9826 | 1.0 | 20 | 2.5250 | 11.8736 | 3.4553 | 8.0701 | 10.4233 | |
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| 2.5516 | 2.0 | 40 | 2.2001 | 15.7664 | 5.0213 | 10.8555 | 14.1975 | |
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| 2.2334 | 3.0 | 60 | 2.0424 | 17.0425 | 6.006 | 10.956 | 15.2795 | |
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| 1.9577 | 4.0 | 80 | 1.9305 | 19.1792 | 7.6754 | 12.651 | 17.7519 | |
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| 1.8602 | 5.0 | 100 | 1.8351 | 22.4846 | 8.3095 | 14.0022 | 20.587 | |
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| 1.702 | 6.0 | 120 | 1.7809 | 21.9395 | 8.5042 | 14.9427 | 20.3436 | |
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| 1.6525 | 7.0 | 140 | 1.7286 | 23.7825 | 10.9231 | 15.9319 | 22.0902 | |
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| 1.5285 | 8.0 | 160 | 1.6839 | 24.1286 | 11.2382 | 16.7057 | 22.3731 | |
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| 1.4623 | 9.0 | 180 | 1.6644 | 23.8767 | 12.3834 | 17.5761 | 22.6869 | |
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| 1.4175 | 10.0 | 200 | 1.6551 | 25.33 | 12.4758 | 18.3801 | 24.0275 | |
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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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