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

This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0018
- F Beta: 0.8127
- Precision: 0.9818
- Recall: 0.8071

## 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: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | F Beta | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:------:|
| 0.0074        | 0.41  | 250  | 0.0022          | 0.9594 | 0.9851    | 0.9584 |
| 0.0031        | 0.82  | 500  | 0.0011          | 0.9541 | 0.9879    | 0.9528 |
| 0.0035        | 1.24  | 750  | 0.0015          | 0.8814 | 0.9869    | 0.8776 |
| 0.0029        | 1.65  | 1000 | 0.0024          | 0.7401 | 0.9849    | 0.7328 |
| 0.0016        | 2.06  | 1250 | 0.0015          | 0.8240 | 0.9810    | 0.8188 |
| 0.0012        | 2.47  | 1500 | 0.0020          | 0.7848 | 0.9812    | 0.7786 |
| 0.003         | 2.88  | 1750 | 0.0018          | 0.8127 | 0.9818    | 0.8071 |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.15.0