update model card README.md
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README.md
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This model is a fine-tuned version of [monologg/kobigbird-bert-base](https://huggingface.co/monologg/kobigbird-bert-base) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss:
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## Model description
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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:
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- eval_batch_size:
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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: linear
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- num_epochs:
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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| 1.8715 | 19.31 | 14000 | 1.9471 |
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| 1.8824 | 20.0 | 14500 | 1.9462 |
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| 1.8261 | 20.69 | 15000 | 1.9412 |
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| 1.815 | 21.38 | 15500 | 1.9308 |
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| 1.7988 | 22.07 | 16000 | 1.9285 |
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| 1.7722 | 22.76 | 16500 | 1.9341 |
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| 1.7606 | 23.45 | 17000 | 1.9007 |
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| 1.7402 | 24.14 | 17500 | 1.9206 |
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| 1.7177 | 24.83 | 18000 | 1.9125 |
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| 1.7117 | 25.52 | 18500 | 1.9178 |
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| 1.6834 | 26.21 | 19000 | 1.9082 |
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| 1.6893 | 26.9 | 19500 | 1.9124 |
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### Framework versions
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- Transformers 4.
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- Pytorch 1.11.0
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- Datasets 2.
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- Tokenizers 0.12.1
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This model is a fine-tuned version of [monologg/kobigbird-bert-base](https://huggingface.co/monologg/kobigbird-bert-base) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.8347
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## Model description
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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: 16
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- eval_batch_size: 16
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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: linear
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- num_epochs: 20
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:-----:|:-----:|:---------------:|
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| 5.3957 | 0.13 | 500 | 3.7603 |
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| 3.2242 | 0.26 | 1000 | 2.3961 |
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| 2.0812 | 0.4 | 1500 | 1.5552 |
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| 1.6198 | 0.53 | 2000 | 1.3609 |
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| 1.447 | 0.66 | 2500 | 1.2270 |
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| 1.3438 | 0.79 | 3000 | 1.1321 |
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| 1.2399 | 0.93 | 3500 | 1.0973 |
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| 1.1976 | 1.06 | 4000 | 1.0418 |
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| 1.1177 | 1.19 | 4500 | 1.0301 |
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| 1.0811 | 1.32 | 5000 | 1.0232 |
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| 1.0506 | 1.45 | 5500 | 0.9971 |
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| 1.0293 | 1.59 | 6000 | 0.9580 |
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| 1.0196 | 1.72 | 6500 | 0.9551 |
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| 0.9846 | 1.85 | 7000 | 0.9274 |
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| 0.9702 | 1.98 | 7500 | 0.9286 |
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| 0.9224 | 2.11 | 8000 | 0.8961 |
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| 0.8867 | 2.25 | 8500 | 0.9193 |
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| 0.8711 | 2.38 | 9000 | 0.8727 |
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| 0.883 | 2.51 | 9500 | 0.8790 |
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| 0.8513 | 2.64 | 10000 | 0.8830 |
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| 0.8709 | 2.78 | 10500 | 0.8604 |
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| 0.8766 | 2.91 | 11000 | 0.8260 |
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| 0.7976 | 3.04 | 11500 | 0.8401 |
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| 0.7724 | 3.17 | 12000 | 0.8617 |
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| 0.78 | 3.3 | 12500 | 0.8601 |
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| 0.7566 | 3.44 | 13000 | 0.8657 |
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| 0.7407 | 3.57 | 13500 | 0.8347 |
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### Framework versions
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- Transformers 4.19.2
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- Pytorch 1.11.0+cu113
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- Datasets 2.2.2
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- Tokenizers 0.12.1
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