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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_toxic_cls |
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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_toxic_cls |
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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: 0.3694 |
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- Accuracy: 0.8054 |
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- Precision: 0.7440 |
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- Recall: 0.9942 |
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- F1: 0.8511 |
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- Auc: 0.8908 |
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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: 32 |
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- eval_batch_size: 8 |
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- seed: 13 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 256 |
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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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- num_epochs: 8 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Auc | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:| |
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| No log | 1.0 | 141 | 0.4441 | 0.8012 | 0.7428 | 0.9861 | 0.8473 | 0.8880 | |
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| No log | 2.0 | 282 | 0.3568 | 0.8042 | 0.7453 | 0.9875 | 0.8495 | 0.8905 | |
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| No log | 3.0 | 423 | 0.3691 | 0.8052 | 0.7444 | 0.9926 | 0.8508 | 0.8922 | |
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| 0.4062 | 4.0 | 564 | 0.3701 | 0.8054 | 0.7440 | 0.9942 | 0.8511 | 0.8908 | |
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| 0.4062 | 5.0 | 705 | 0.3925 | 0.8051 | 0.7436 | 0.9944 | 0.8509 | 0.8915 | |
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| 0.4062 | 6.0 | 846 | 0.3891 | 0.8056 | 0.7498 | 0.9793 | 0.8493 | 0.8921 | |
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| 0.4062 | 7.0 | 987 | 0.3860 | 0.8070 | 0.7573 | 0.9638 | 0.8482 | 0.8943 | |
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| 0.3208 | 8.0 | 1128 | 0.3909 | 0.8073 | 0.7603 | 0.9575 | 0.8475 | 0.8939 | |
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### Framework versions |
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- Transformers 4.57.1 |
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- Pytorch 2.8.0+cu129 |
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- Datasets 4.4.1 |
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- Tokenizers 0.22.1 |
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