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--- |
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library_name: transformers |
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license: mit |
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base_model: microsoft/mdeberta-v3-base |
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tags: |
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- generated_from_trainer |
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metrics: |
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- f1 |
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- accuracy |
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model-index: |
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- name: CS221-mdeberta-v3-base-randomdrop |
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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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# CS221-mdeberta-v3-base-randomdrop |
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This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5440 |
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- F1: 0.6741 |
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- Roc Auc: 0.7756 |
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- Accuracy: 0.4071 |
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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: 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: cosine |
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- lr_scheduler_warmup_steps: 100 |
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- num_epochs: 20 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:| |
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| 0.5661 | 1.0 | 99 | 0.5434 | 0.0 | 0.5 | 0.1425 | |
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| 0.5054 | 2.0 | 198 | 0.4744 | 0.4852 | 0.6560 | 0.2621 | |
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| 0.4409 | 3.0 | 297 | 0.4436 | 0.5766 | 0.7104 | 0.3308 | |
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| 0.3975 | 4.0 | 396 | 0.4284 | 0.6071 | 0.7316 | 0.3588 | |
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| 0.2827 | 5.0 | 495 | 0.4228 | 0.6095 | 0.7296 | 0.3562 | |
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| 0.2831 | 6.0 | 594 | 0.4540 | 0.6467 | 0.7642 | 0.3715 | |
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| 0.1846 | 7.0 | 693 | 0.4519 | 0.6325 | 0.7459 | 0.3893 | |
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| 0.1752 | 8.0 | 792 | 0.4538 | 0.6426 | 0.7535 | 0.3740 | |
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| 0.1547 | 9.0 | 891 | 0.4799 | 0.6541 | 0.7642 | 0.3791 | |
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| 0.1046 | 10.0 | 990 | 0.4793 | 0.6667 | 0.7687 | 0.4020 | |
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| 0.1052 | 11.0 | 1089 | 0.5001 | 0.6593 | 0.7658 | 0.4046 | |
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| 0.0843 | 12.0 | 1188 | 0.5069 | 0.6647 | 0.7705 | 0.3893 | |
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| 0.0653 | 13.0 | 1287 | 0.5275 | 0.6681 | 0.7669 | 0.4097 | |
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| 0.0575 | 14.0 | 1386 | 0.5455 | 0.6617 | 0.7632 | 0.3944 | |
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| 0.0503 | 15.0 | 1485 | 0.5440 | 0.6741 | 0.7756 | 0.4071 | |
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| 0.0499 | 16.0 | 1584 | 0.5555 | 0.6653 | 0.7660 | 0.4097 | |
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| 0.0431 | 17.0 | 1683 | 0.5557 | 0.6660 | 0.7675 | 0.4020 | |
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| 0.0422 | 18.0 | 1782 | 0.5599 | 0.6632 | 0.7664 | 0.3944 | |
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### Framework versions |
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- Transformers 4.47.1 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 3.2.0 |
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- Tokenizers 0.21.0 |
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