---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
base_model: google/bert_uncased_L-8_H-512_A-8
model-index:
- name: pii_mini
  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_mini

This model is a fine-tuned version of [google/bert_uncased_L-8_H-512_A-8](https://huggingface.co/google/bert_uncased_L-8_H-512_A-8) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1113
- Precision: 0.9001
- Recall: 0.9290
- F1: 0.9143
- Accuracy: 0.9645

## 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: 3e-05
- train_batch_size: 256
- eval_batch_size: 256
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 153  | 0.3797          | 0.4943    | 0.5523 | 0.5217 | 0.9024   |
| No log        | 2.0   | 306  | 0.1868          | 0.7281    | 0.7917 | 0.7586 | 0.9419   |
| No log        | 3.0   | 459  | 0.1319          | 0.8339    | 0.8735 | 0.8532 | 0.9565   |
| 0.5069        | 4.0   | 612  | 0.1098          | 0.8690    | 0.8990 | 0.8837 | 0.9603   |
| 0.5069        | 5.0   | 765  | 0.0971          | 0.8725    | 0.9082 | 0.8900 | 0.9647   |
| 0.5069        | 6.0   | 918  | 0.0924          | 0.8887    | 0.9179 | 0.9031 | 0.9653   |
| 0.1032        | 7.0   | 1071 | 0.0920          | 0.8820    | 0.9175 | 0.8994 | 0.9632   |
| 0.1032        | 8.0   | 1224 | 0.0869          | 0.8886    | 0.9219 | 0.9050 | 0.9652   |
| 0.1032        | 9.0   | 1377 | 0.0912          | 0.8917    | 0.9235 | 0.9073 | 0.9649   |
| 0.0719        | 10.0  | 1530 | 0.0875          | 0.8995    | 0.9271 | 0.9131 | 0.9666   |
| 0.0719        | 11.0  | 1683 | 0.0964          | 0.8971    | 0.9264 | 0.9115 | 0.9649   |
| 0.0719        | 12.0  | 1836 | 0.1006          | 0.9030    | 0.9293 | 0.9159 | 0.9656   |
| 0.0719        | 13.0  | 1989 | 0.1011          | 0.8978    | 0.9291 | 0.9132 | 0.9639   |
| 0.0539        | 14.0  | 2142 | 0.1071          | 0.9007    | 0.9275 | 0.9139 | 0.9628   |
| 0.0539        | 15.0  | 2295 | 0.1113          | 0.9001    | 0.9290 | 0.9143 | 0.9645   |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.1