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--- |
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base_model: klue/roberta-large |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: 0320_cosmetic2_roberta |
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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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# 0320_cosmetic2_roberta |
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This model is a fine-tuned version of [klue/roberta-large](https://huggingface.co/klue/roberta-large) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4218 |
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- Accuracy: 0.8535 |
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- F1: 0.8554 |
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- Precision: 0.8632 |
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- Recall: 0.8535 |
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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: 8 |
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- eval_batch_size: 8 |
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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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- 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 | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| 0.4995 | 1.0 | 273 | 0.3611 | 0.8713 | 0.8724 | 0.8767 | 0.8713 | |
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| 0.4616 | 2.0 | 546 | 0.4809 | 0.8419 | 0.8435 | 0.8658 | 0.8419 | |
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| 0.2517 | 3.0 | 819 | 0.7009 | 0.8640 | 0.8651 | 0.8772 | 0.8640 | |
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| 0.6884 | 4.0 | 1092 | 0.7427 | 0.7978 | 0.8007 | 0.8425 | 0.7978 | |
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| 0.4318 | 5.0 | 1365 | 0.4725 | 0.8640 | 0.8647 | 0.8660 | 0.8640 | |
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| 0.2824 | 6.0 | 1638 | 0.6081 | 0.875 | 0.8759 | 0.8798 | 0.875 | |
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| 0.317 | 7.0 | 1911 | 0.5933 | 0.8676 | 0.8665 | 0.8672 | 0.8676 | |
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| 0.2067 | 8.0 | 2184 | 0.6951 | 0.8676 | 0.8671 | 0.8702 | 0.8676 | |
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| 0.037 | 9.0 | 2457 | 0.6081 | 0.8860 | 0.8851 | 0.8896 | 0.8860 | |
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| 0.1009 | 10.0 | 2730 | 0.7525 | 0.8640 | 0.8642 | 0.8651 | 0.8640 | |
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### Framework versions |
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- Transformers 4.38.1 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.17.1 |
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- Tokenizers 0.15.2 |
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