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--- |
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license: mit |
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base_model: gogamza/kobart-base-v2 |
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tags: |
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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: kobart-base-v2-159-korean |
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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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# kobart-base-v2-159-korean |
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This model is a fine-tuned version of [gogamza/kobart-base-v2](https://huggingface.co./gogamza/kobart-base-v2) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2628 |
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- Rouge1: 0.3634 |
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- Rouge2: 0.1451 |
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- Rougel: 0.3566 |
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- Rougelsum: 0.3563 |
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- Gen Len: 20.0 |
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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: 5e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- gradient_accumulation_steps: 16 |
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- total_train_batch_size: 64 |
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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: 500 |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| |
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| 0.2804 | 1.44 | 500 | 0.2805 | 0.3321 | 0.1273 | 0.3272 | 0.3276 | 19.9992 | |
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| 0.2141 | 2.88 | 1000 | 0.2472 | 0.3577 | 0.1381 | 0.3526 | 0.3525 | 20.0 | |
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| 0.1407 | 4.33 | 1500 | 0.2495 | 0.3615 | 0.1457 | 0.3543 | 0.3543 | 20.0 | |
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| 0.1206 | 5.77 | 2000 | 0.2508 | 0.3592 | 0.1448 | 0.3533 | 0.3532 | 20.0 | |
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| 0.0853 | 7.21 | 2500 | 0.2603 | 0.3623 | 0.147 | 0.3561 | 0.3562 | 20.0 | |
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| 0.0777 | 8.65 | 3000 | 0.2628 | 0.3634 | 0.1451 | 0.3566 | 0.3563 | 20.0 | |
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### Framework versions |
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- Transformers 4.36.2 |
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- Pytorch 2.1.2+cu121 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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