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---
library_name: transformers
license: mit
base_model: microsoft/deberta-v3-large
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: deberta-pii-masking-augmented-test5
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. -->
# deberta-pii-masking-augmented-test5
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0360
- Precision: 0.9402
- Recall: 0.9557
- F1: 0.9479
- Accuracy: 0.9891
## 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: 1e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.6188 | 0.0305 | 500 | 0.3788 | 0.5431 | 0.6547 | 0.5937 | 0.9030 |
| 0.1746 | 0.0609 | 1000 | 0.1576 | 0.7686 | 0.8381 | 0.8019 | 0.9565 |
| 0.0857 | 0.0914 | 1500 | 0.1094 | 0.8148 | 0.8787 | 0.8455 | 0.9669 |
| 0.0624 | 0.1219 | 2000 | 0.0882 | 0.8475 | 0.8979 | 0.8720 | 0.9728 |
| 0.0512 | 0.1524 | 2500 | 0.0610 | 0.8834 | 0.9161 | 0.8994 | 0.9811 |
| 0.0445 | 0.1828 | 3000 | 0.0584 | 0.8968 | 0.9216 | 0.9090 | 0.9814 |
| 0.0398 | 0.2133 | 3500 | 0.0545 | 0.9097 | 0.9324 | 0.9209 | 0.9836 |
| 0.0355 | 0.2438 | 4000 | 0.0500 | 0.9125 | 0.9342 | 0.9232 | 0.9845 |
| 0.0337 | 0.2743 | 4500 | 0.0477 | 0.9068 | 0.9355 | 0.9209 | 0.9843 |
| 0.0309 | 0.3047 | 5000 | 0.0489 | 0.9214 | 0.9408 | 0.9310 | 0.9854 |
| 0.0284 | 0.3352 | 5500 | 0.0444 | 0.9173 | 0.9433 | 0.9301 | 0.9861 |
| 0.0278 | 0.3657 | 6000 | 0.0423 | 0.9247 | 0.9416 | 0.9331 | 0.9865 |
| 0.0258 | 0.3962 | 6500 | 0.0410 | 0.9291 | 0.9471 | 0.9380 | 0.9873 |
| 0.0242 | 0.4266 | 7000 | 0.0375 | 0.9301 | 0.9499 | 0.9399 | 0.9881 |
| 0.0241 | 0.4571 | 7500 | 0.0380 | 0.9321 | 0.9500 | 0.9410 | 0.9882 |
| 0.0217 | 0.4876 | 8000 | 0.0347 | 0.9404 | 0.9545 | 0.9474 | 0.9890 |
| 0.0207 | 0.5181 | 8500 | 0.0335 | 0.9360 | 0.9526 | 0.9442 | 0.9892 |
| 0.0204 | 0.5485 | 9000 | 0.0366 | 0.9364 | 0.9542 | 0.9452 | 0.9888 |
| 0.019 | 0.5790 | 9500 | 0.0362 | 0.9355 | 0.9534 | 0.9444 | 0.9887 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
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