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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-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: deberta-v3-large-csb |
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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-v3-large-csb |
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This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2608 |
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- Accuracy: 0.8813 |
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- F1: 0.8805 |
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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: 1e-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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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 16 |
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- optimizer: Use adamw_torch 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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- 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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| 0.4418 | 1.0 | 228 | 0.3463 | 0.8396 | 0.8397 | |
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| 0.3375 | 2.0 | 456 | 0.2615 | 0.8703 | 0.8705 | |
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| 0.2706 | 3.0 | 684 | 0.2608 | 0.8813 | 0.8805 | |
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| 0.2298 | 4.0 | 912 | 0.3437 | 0.8791 | 0.8780 | |
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| 0.1609 | 5.0 | 1140 | 0.6636 | 0.8132 | 0.8050 | |
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| 0.1665 | 6.0 | 1368 | 0.5089 | 0.8791 | 0.8791 | |
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| 0.099 | 7.0 | 1596 | 0.6432 | 0.8813 | 0.8804 | |
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| 0.075 | 8.0 | 1824 | 0.7101 | 0.8747 | 0.8741 | |
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| 0.044 | 9.0 | 2052 | 0.7694 | 0.8681 | 0.8673 | |
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| 0.0478 | 10.0 | 2280 | 0.8504 | 0.8593 | 0.8573 | |
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### Framework versions |
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- Transformers 4.57.3 |
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- Pytorch 2.2.1 |
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- Datasets 4.4.1 |
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- Tokenizers 0.22.1 |
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