---
license: apache-2.0
base_model: distilbert-base-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-cased-pii-en
  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. -->

# distilbert-base-cased-pii-en

This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0412
- Bod F1: 0.9572
- Building F1: 0.9765
- Cardissuer F1: 0.0
- City F1: 0.9467
- Country F1: 0.9664
- Date F1: 0.9008
- Driverlicense F1: 0.9304
- Email F1: 0.9844
- Geocoord F1: 0.9655
- Givenname1 F1: 0.8097
- Givenname2 F1: 0.5922
- Idcard F1: 0.9202
- Ip F1: 0.9807
- Lastname1 F1: 0.7518
- Lastname2 F1: 0.4932
- Lastname3 F1: 0.0948
- Pass F1: 0.8835
- Passport F1: 0.9392
- Postcode F1: 0.9766
- Secaddress F1: 0.9749
- Sex F1: 0.9687
- Socialnumber F1: 0.9334
- State F1: 0.9744
- Street F1: 0.9534
- Tel F1: 0.9553
- Time F1: 0.9619
- Title F1: 0.9502
- Username F1: 0.9495
- Precision: 0.9163
- Recall: 0.9342
- F1: 0.9252
- Accuracy: 0.9903

## 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: 64
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.2
- lr_scheduler_warmup_steps: 3000
- num_epochs: 10

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Bod F1 | Building F1 | Cardissuer F1 | City F1 | Country F1 | Date F1 | Driverlicense F1 | Email F1 | Geocoord F1 | Givenname1 F1 | Givenname2 F1 | Idcard F1 | Ip F1  | Lastname1 F1 | Lastname2 F1 | Lastname3 F1 | Pass F1 | Passport F1 | Postcode F1 | Secaddress F1 | Sex F1 | Socialnumber F1 | State F1 | Street F1 | Tel F1 | Time F1 | Title F1 | Username F1 | Precision | Recall | F1     | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:------:|:-----------:|:-------------:|:-------:|:----------:|:-------:|:----------------:|:--------:|:-----------:|:-------------:|:-------------:|:---------:|:------:|:------------:|:------------:|:------------:|:-------:|:-----------:|:-----------:|:-------------:|:------:|:---------------:|:--------:|:---------:|:------:|:-------:|:--------:|:-----------:|:---------:|:------:|:------:|:--------:|
| 0.2231        | 2.1368 | 1000 | 0.1075          | 0.8895 | 0.9243      | 0.0           | 0.6385  | 0.8816     | 0.7987  | 0.6178           | 0.9512   | 0.6982      | 0.4720        | 0.0           | 0.5863    | 0.9082 | 0.5397       | 0.0          | 0.0          | 0.6402  | 0.6167      | 0.7858      | 0.6568        | 0.8626 | 0.7003          | 0.8859   | 0.6843    | 0.8146 | 0.9158  | 0.7302   | 0.8258      | 0.7239    | 0.7677 | 0.7452 | 0.9739   |
| 0.069         | 4.2735 | 2000 | 0.0540          | 0.9478 | 0.9698      | 0.0           | 0.9055  | 0.9433     | 0.8854  | 0.8801           | 0.9783   | 0.9676      | 0.7201        | 0.2896        | 0.8815    | 0.9731 | 0.6380       | 0.1939       | 0.0          | 0.8266  | 0.8883      | 0.9592      | 0.9645        | 0.9370 | 0.8931          | 0.9390   | 0.9237    | 0.9386 | 0.9455  | 0.9087   | 0.9195      | 0.8707    | 0.9044 | 0.8872 | 0.9865   |
| 0.0447        | 6.4103 | 3000 | 0.0455          | 0.9537 | 0.9756      | 0.0           | 0.9327  | 0.9593     | 0.9007  | 0.9030           | 0.9792   | 0.9633      | 0.7860        | 0.4337        | 0.9056    | 0.9747 | 0.7205       | 0.3587       | 0.0          | 0.8557  | 0.9144      | 0.9712      | 0.9732        | 0.9661 | 0.9204          | 0.9689   | 0.9426    | 0.9552 | 0.9588  | 0.9374   | 0.9413      | 0.9011    | 0.9232 | 0.9120 | 0.9887   |
| 0.0293        | 8.5470 | 4000 | 0.0412          | 0.9572 | 0.9765      | 0.0           | 0.9467  | 0.9664     | 0.9008  | 0.9304           | 0.9844   | 0.9655      | 0.8097        | 0.5922        | 0.9202    | 0.9807 | 0.7518       | 0.4932       | 0.0948       | 0.8835  | 0.9392      | 0.9766      | 0.9749        | 0.9687 | 0.9334          | 0.9744   | 0.9534    | 0.9553 | 0.9619  | 0.9502   | 0.9495      | 0.9163    | 0.9342 | 0.9252 | 0.9903   |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1