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README.md
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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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- precision
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- recall
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- f1
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model-index:
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- name: deberta-base
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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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# deberta-base
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This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1665
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- Accuracy: 0.9601
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- Precision: 0.9599
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- Recall: 0.9601
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- F1: 0.9594
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- Auroc: 0.9928
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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: 16
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- eval_batch_size: 8
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 64
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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: 1
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- label_smoothing_factor: 0.03
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Auroc |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
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| 0.4866 | 0.0988 | 256 | 0.2931 | 0.8845 | 0.8939 | 0.8845 | 0.8876 | 0.9465 |
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| 0.2757 | 0.1977 | 512 | 0.3478 | 0.8898 | 0.8984 | 0.8898 | 0.8765 | 0.9544 |
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| 0.2433 | 0.2965 | 768 | 0.2097 | 0.9404 | 0.9413 | 0.9404 | 0.9408 | 0.9799 |
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| 0.2332 | 0.3953 | 1024 | 0.3548 | 0.8815 | 0.8907 | 0.8815 | 0.8657 | 0.9690 |
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| 0.2152 | 0.4942 | 1280 | 0.1942 | 0.9440 | 0.9434 | 0.9440 | 0.9426 | 0.9868 |
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| 0.1907 | 0.5930 | 1536 | 0.1615 | 0.9649 | 0.9647 | 0.9649 | 0.9647 | 0.9899 |
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| 0.1865 | 0.6918 | 1792 | 0.1556 | 0.9655 | 0.9654 | 0.9655 | 0.9654 | 0.9922 |
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| 0.1865 | 0.7907 | 2048 | 0.2322 | 0.9369 | 0.9370 | 0.9369 | 0.9344 | 0.9773 |
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| 0.168 | 0.8895 | 2304 | 0.1653 | 0.9672 | 0.9670 | 0.9672 | 0.9668 | 0.9937 |
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| 0.1732 | 0.9883 | 2560 | 0.1467 | 0.9702 | 0.9716 | 0.9702 | 0.9706 | 0.9935 |
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### Framework versions
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- Transformers 4.45.1
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- Pytorch 2.4.0
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- Datasets 3.0.1
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- Tokenizers 0.20.0
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