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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-small |
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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: Noisy10per-deberta-v3-small-Label_B-768-epochs-9 |
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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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# Noisy10per-deberta-v3-small-Label_B-768-epochs-9 |
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This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1297 |
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- Accuracy: 0.9854 |
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- F1: 0.9854 |
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- Precision: 0.9856 |
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- Recall: 0.9854 |
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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: 12 |
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- eval_batch_size: 12 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 48 |
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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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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 9 |
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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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| 0.0764 | 0.9995 | 1066 | 0.1081 | 0.9730 | 0.9731 | 0.9733 | 0.9730 | |
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| 0.083 | 1.9993 | 2132 | 0.1059 | 0.9763 | 0.9762 | 0.9770 | 0.9763 | |
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| 0.0474 | 2.9991 | 3198 | 0.0775 | 0.9833 | 0.9833 | 0.9834 | 0.9833 | |
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| 0.0028 | 3.9998 | 4265 | 0.1005 | 0.9818 | 0.9818 | 0.9821 | 0.9818 | |
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| 0.0025 | 4.9995 | 5331 | 0.1092 | 0.9841 | 0.9842 | 0.9843 | 0.9841 | |
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| 0.0287 | 5.9993 | 6397 | 0.1633 | 0.9820 | 0.9821 | 0.9827 | 0.9820 | |
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| 0.0085 | 6.9991 | 7463 | 0.1640 | 0.9814 | 0.9814 | 0.9818 | 0.9814 | |
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| 0.001 | 7.9998 | 8530 | 0.1297 | 0.9854 | 0.9854 | 0.9856 | 0.9854 | |
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| 0.0 | 8.9977 | 9594 | 0.1368 | 0.9851 | 0.9852 | 0.9853 | 0.9851 | |
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### Framework versions |
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- Transformers 4.46.3 |
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- Pytorch 2.4.0 |
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- Datasets 3.1.0 |
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- Tokenizers 0.20.3 |
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