---
license: mit
base_model: microsoft/deberta-v3-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: pii-ner
  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-ner

This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5816
- Precision: 0.3789
- Recall: 0.2054
- F1: 0.2664
- Accuracy: 0.9580

## 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: 8
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.3048        | 1.0   | 14   | 0.7370          | 0.0088    | 0.0438 | 0.0147 | 0.7632   |
| 0.7328        | 2.0   | 28   | 0.2553          | 0.0       | 0.0    | 0.0    | 0.9350   |
| 0.2676        | 3.0   | 42   | 0.2095          | 0.7353    | 0.1684 | 0.2740 | 0.9658   |
| 0.0976        | 4.0   | 56   | 0.2309          | 0.9091    | 0.2020 | 0.3306 | 0.9683   |
| 0.0664        | 5.0   | 70   | 0.2471          | 0.9242    | 0.2054 | 0.3361 | 0.9685   |
| 0.1703        | 6.0   | 84   | 0.2893          | 0.9091    | 0.2020 | 0.3306 | 0.9683   |
| 0.1196        | 7.0   | 98   | 0.2559          | 0.8841    | 0.2054 | 0.3333 | 0.9683   |
| 0.1805        | 8.0   | 112  | 0.2691          | 0.9091    | 0.2020 | 0.3306 | 0.9683   |
| 0.1669        | 9.0   | 126  | 0.2651          | 0.9242    | 0.2054 | 0.3361 | 0.9685   |
| 0.2296        | 10.0  | 140  | 0.3022          | 0.9242    | 0.2054 | 0.3361 | 0.9685   |
| 0.1731        | 11.0  | 154  | 0.2879          | 0.9091    | 0.2020 | 0.3306 | 0.9682   |
| 0.1983        | 12.0  | 168  | 0.2824          | 0.8824    | 0.2020 | 0.3288 | 0.9682   |
| 0.0873        | 13.0  | 182  | 0.3495          | 0.9091    | 0.2020 | 0.3306 | 0.9683   |
| 0.4           | 14.0  | 196  | 0.3905          | 0.9104    | 0.2054 | 0.3352 | 0.9683   |
| 0.2142        | 15.0  | 210  | 0.4198          | 0.9104    | 0.2054 | 0.3352 | 0.9683   |
| 0.2092        | 16.0  | 224  | 0.4395          | 0.9091    | 0.2020 | 0.3306 | 0.9682   |
| 0.0803        | 17.0  | 238  | 0.4003          | 0.9104    | 0.2054 | 0.3352 | 0.9682   |
| 0.1509        | 18.0  | 252  | 0.4904          | 0.9104    | 0.2054 | 0.3352 | 0.9683   |
| 0.1382        | 19.0  | 266  | 0.5018          | 0.9091    | 0.2020 | 0.3306 | 0.9682   |
| 0.0554        | 20.0  | 280  | 0.5128          | 0.9104    | 0.2054 | 0.3352 | 0.9682   |
| 0.0595        | 21.0  | 294  | 0.5363          | 0.9104    | 0.2054 | 0.3352 | 0.9682   |
| 0.0339        | 22.0  | 308  | 0.5518          | 0.9104    | 0.2054 | 0.3352 | 0.9682   |
| 0.0292        | 23.0  | 322  | 0.5613          | 0.8133    | 0.2054 | 0.3280 | 0.9674   |
| 0.0368        | 24.0  | 336  | 0.5555          | 0.9104    | 0.2054 | 0.3352 | 0.9682   |
| 0.0224        | 25.0  | 350  | 0.5709          | 0.3885    | 0.2054 | 0.2687 | 0.9585   |
| 0.0122        | 26.0  | 364  | 0.5753          | 0.3789    | 0.2054 | 0.2664 | 0.9580   |
| 0.0332        | 27.0  | 378  | 0.5800          | 0.3789    | 0.2054 | 0.2664 | 0.9580   |
| 0.02          | 28.0  | 392  | 0.5810          | 0.3789    | 0.2054 | 0.2664 | 0.9580   |
| 0.0155        | 29.0  | 406  | 0.5816          | 0.3789    | 0.2054 | 0.2664 | 0.9580   |
| 0.0157        | 30.0  | 420  | 0.5816          | 0.3789    | 0.2054 | 0.2664 | 0.9580   |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2