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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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- precision
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- recall
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- f1
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model-index:
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- name: SensiGuard-PII
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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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# SensiGuard-PII
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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: 0.0067
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- Precision: 0.6437
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- Recall: 0.9659
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- F1: 0.7726
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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: 64
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- eval_batch_size: 64
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- seed: 42
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- optimizer: Use OptimizerNames.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: 2
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
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| 0.0148 | 1.0 | 4650 | 0.0099 | 0.6266 | 0.9636 | 0.7594 |
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| 0.0018 | 2.0 | 9300 | 0.0067 | 0.6437 | 0.9659 | 0.7726 |
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### Framework versions
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- Transformers 4.57.3
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- Pytorch 2.6.0+rocm6.1
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- Datasets 4.4.1
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- Tokenizers 0.22.1
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