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--- |
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library_name: transformers |
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license: mit |
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base_model: microsoft/deberta-v3-base |
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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: polar4 |
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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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# polar4 |
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This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5187 |
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- Accuracy: 0.7426 |
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- F1: 0.7199 |
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- Precision: 0.7477 |
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- Recall: 0.7426 |
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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: 0.0002 |
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- train_batch_size: 200 |
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- eval_batch_size: 200 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.2 |
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- num_epochs: 50 |
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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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| 0.6359 | 7.6923 | 100 | 0.6229 | 0.6357 | 0.4941 | 0.4041 | 0.6357 | |
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| 0.5953 | 15.3846 | 200 | 0.5654 | 0.7039 | 0.6698 | 0.7030 | 0.7039 | |
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| 0.5605 | 23.0769 | 300 | 0.5510 | 0.6930 | 0.6297 | 0.7259 | 0.6930 | |
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| 0.5578 | 30.7692 | 400 | 0.5273 | 0.7426 | 0.7384 | 0.7376 | 0.7426 | |
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| 0.5635 | 38.4615 | 500 | 0.5198 | 0.7364 | 0.7257 | 0.7301 | 0.7364 | |
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| 0.5443 | 46.1538 | 600 | 0.5214 | 0.7271 | 0.6978 | 0.7338 | 0.7271 | |
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### Framework versions |
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- Transformers 4.57.1 |
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- Pytorch 2.8.0+cu126 |
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- Datasets 4.4.1 |
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- Tokenizers 0.22.1 |
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