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library_name: transformers
license: mit
base_model: microsoft/deberta-v3-small
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: PII-Token-Filter-Hard-V1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# PII-Token-Filter-Hard-V1
This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9765
- Precision: 0.2376
- Recall: 0.9901
- F1: 0.3832
- Accuracy: 0.7629
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0147 | 1.0 | 2651 | 1.9499 | 0.2339 | 0.9274 | 0.3735 | 0.7659 |
| 0.0065 | 2.0 | 5302 | 2.4256 | 0.2371 | 0.9862 | 0.3823 | 0.7620 |
| 0.003 | 3.0 | 7953 | 2.6138 | 0.2390 | 0.9833 | 0.3845 | 0.7651 |
| 0.0013 | 4.0 | 10604 | 2.8710 | 0.2366 | 0.9869 | 0.3817 | 0.7624 |
| 0.0005 | 5.0 | 13255 | 2.9765 | 0.2376 | 0.9901 | 0.3832 | 0.7629 |
### Framework versions
- Transformers 4.56.0
- Pytorch 2.8.0+cu129
- Datasets 4.8.2
- Tokenizers 0.22.0
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