---
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 was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0033
- F Beta: 0.7862
- Precision: 0.9914
- Recall: 0.7797

## 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: 5e-06
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 3
- total_train_batch_size: 24
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 1
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | F Beta | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:------:|
| 0.0001        | 0.27  | 70   | 0.0119          | 0.3263 | 0.9775    | 0.3178 |
| 0.002         | 0.54  | 140  | 0.0044          | 0.7110 | 0.9904    | 0.7030 |
| 0.0003        | 0.82  | 210  | 0.0033          | 0.7862 | 0.9914    | 0.7797 |


### Framework versions

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