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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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model-index: |
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- name: pogny-16-0.00002 |
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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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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/bella05/huggingface/runs/gi9imxak) |
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# pogny-16-0.00002 |
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This model is a fine-tuned version of [klue/roberta-large](https://huggingface.co/klue/roberta-large) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.9252 |
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- Accuracy: 0.7676 |
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- F1: 0.7648 |
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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: 2e-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: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:| |
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| 0.6579 | 1.0 | 4818 | 0.6370 | 0.7627 | 0.7604 | |
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| 0.5424 | 2.0 | 9636 | 0.6263 | 0.7671 | 0.7636 | |
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| 0.4158 | 3.0 | 14454 | 0.6761 | 0.7721 | 0.7690 | |
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| 0.3116 | 4.0 | 19272 | 0.7796 | 0.7705 | 0.7680 | |
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| 0.221 | 5.0 | 24090 | 1.0186 | 0.7640 | 0.7616 | |
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| 0.1605 | 6.0 | 28908 | 1.3264 | 0.7679 | 0.7648 | |
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| 0.122 | 7.0 | 33726 | 1.5300 | 0.7685 | 0.7642 | |
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| 0.0808 | 8.0 | 38544 | 1.7068 | 0.7594 | 0.7574 | |
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| 0.0606 | 9.0 | 43362 | 1.8555 | 0.7632 | 0.7613 | |
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| 0.0249 | 10.0 | 48180 | 1.9252 | 0.7676 | 0.7648 | |
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### Framework versions |
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- Transformers 4.41.0 |
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- Pytorch 2.2.2 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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