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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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model-index: |
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- name: KoBART_base_v2-trial |
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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-trial |
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This model is a fine-tuned version of [gogamza/kobart-base-v2](https://huggingface.co./gogamza/kobart-base-v2) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1815 |
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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: 0.0005 |
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- train_batch_size: 64 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 20 |
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- num_epochs: 3 |
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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 | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 2.4147 | 0.11 | 50 | 0.5490 | |
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| 0.5457 | 0.22 | 100 | 0.4810 | |
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| 0.4642 | 0.32 | 150 | 0.3971 | |
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| 0.4364 | 0.43 | 200 | 0.3955 | |
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| 0.4111 | 0.54 | 250 | 0.3851 | |
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| 0.3888 | 0.65 | 300 | 0.3438 | |
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| 0.3586 | 0.76 | 350 | 0.3290 | |
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| 0.3304 | 0.87 | 400 | 0.3201 | |
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| 0.3337 | 0.97 | 450 | 0.2992 | |
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| 0.2677 | 1.08 | 500 | 0.3161 | |
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| 0.2576 | 1.19 | 550 | 0.2981 | |
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| 0.2467 | 1.3 | 600 | 0.2846 | |
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| 0.2369 | 1.41 | 650 | 0.2674 | |
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| 0.226 | 1.52 | 700 | 0.2529 | |
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| 0.2204 | 1.62 | 750 | 0.2446 | |
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| 0.204 | 1.73 | 800 | 0.2400 | |
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| 0.2071 | 1.84 | 850 | 0.2262 | |
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| 0.1911 | 1.95 | 900 | 0.2153 | |
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| 0.1591 | 2.06 | 950 | 0.2121 | |
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| 0.1338 | 2.16 | 1000 | 0.2090 | |
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| 0.1312 | 2.27 | 1050 | 0.1986 | |
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| 0.1336 | 2.38 | 1100 | 0.1947 | |
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| 0.1205 | 2.49 | 1150 | 0.1903 | |
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| 0.1162 | 2.6 | 1200 | 0.1867 | |
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| 0.1187 | 2.71 | 1250 | 0.1840 | |
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| 0.1171 | 2.81 | 1300 | 0.1821 | |
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| 0.1149 | 2.92 | 1350 | 0.1815 | |
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### Framework versions |
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- Transformers 4.36.0 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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