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---
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: 0.0635
- Bod F1: 0.9622
- Building F1: 0.9808
- Cardissuer F1: 0.0
- City F1: 0.9589
- Country F1: 0.9741
- Date F1: 0.9176
- Driverlicense F1: 0.9355
- Email F1: 0.9855
- Geocoord F1: 0.9814
- Givenname1 F1: 0.8496
- Givenname2 F1: 0.7261
- Idcard F1: 0.9306
- Ip F1: 0.9850
- Lastname1 F1: 0.7974
- Lastname2 F1: 0.6672
- Lastname3 F1: 0.6393
- Pass F1: 0.9005
- Passport F1: 0.9405
- Postcode F1: 0.9794
- Secaddress F1: 0.9745
- Sex F1: 0.9738
- Socialnumber F1: 0.9437
- State F1: 0.9794
- Street F1: 0.9651
- Tel F1: 0.9674
- Time F1: 0.9638
- Title F1: 0.9587
- Username F1: 0.9469
- Precision: 0.9318
- Recall: 0.9460
- F1: 0.9388
- Accuracy: 0.9913
## 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
- training_steps: 20000
### 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 |
|:-------------:|:-------:|:-----:|:---------------:|:------:|:-----------:|:-------------:|:-------:|:----------:|:-------:|:----------------:|:--------:|:-----------:|:-------------:|:-------------:|:---------:|:------:|:------------:|:------------:|:------------:|:-------:|:-----------:|:-----------:|:-------------:|:------:|:---------------:|:--------:|:---------:|:------:|:-------:|:--------:|:-----------:|:---------:|:------:|:------:|:--------:|
| 0.23 | 2.1368 | 1000 | 0.1111 | 0.8804 | 0.9218 | 0.0 | 0.5854 | 0.8890 | 0.8009 | 0.5896 | 0.9469 | 0.7002 | 0.4149 | 0.0 | 0.5740 | 0.9056 | 0.5230 | 0.0 | 0.0 | 0.6551 | 0.6301 | 0.7819 | 0.5871 | 0.8396 | 0.7137 | 0.8771 | 0.6466 | 0.8567 | 0.9185 | 0.7411 | 0.8202 | 0.7127 | 0.7640 | 0.7374 | 0.9723 |
| 0.0687 | 4.2735 | 2000 | 0.0541 | 0.9365 | 0.9688 | 0.0 | 0.9115 | 0.9434 | 0.8841 | 0.8530 | 0.9768 | 0.9698 | 0.7276 | 0.3073 | 0.8720 | 0.9701 | 0.6430 | 0.1854 | 0.0 | 0.8169 | 0.8857 | 0.9638 | 0.9617 | 0.9312 | 0.8927 | 0.9418 | 0.9231 | 0.9438 | 0.9461 | 0.9232 | 0.9138 | 0.8698 | 0.9020 | 0.8856 | 0.9862 |
| 0.0452 | 6.4103 | 3000 | 0.0469 | 0.9550 | 0.9753 | 0.0 | 0.9404 | 0.9620 | 0.9014 | 0.9004 | 0.9831 | 0.9721 | 0.7664 | 0.4606 | 0.9099 | 0.97 | 0.6838 | 0.3818 | 0.0 | 0.8611 | 0.9079 | 0.9757 | 0.9715 | 0.9661 | 0.9197 | 0.9674 | 0.9435 | 0.9448 | 0.96 | 0.9354 | 0.9357 | 0.8975 | 0.9212 | 0.9092 | 0.9883 |
| 0.0311 | 8.5470 | 4000 | 0.0434 | 0.9581 | 0.9753 | 0.0 | 0.9481 | 0.9641 | 0.8973 | 0.9217 | 0.9833 | 0.9745 | 0.8085 | 0.6085 | 0.9232 | 0.9781 | 0.7519 | 0.4891 | 0.0849 | 0.8685 | 0.9280 | 0.9771 | 0.9704 | 0.9659 | 0.9278 | 0.9729 | 0.9549 | 0.9563 | 0.9594 | 0.9564 | 0.9407 | 0.9117 | 0.9344 | 0.9229 | 0.9897 |
| 0.023 | 10.6838 | 5000 | 0.0416 | 0.9578 | 0.9778 | 0.0 | 0.9503 | 0.9639 | 0.9043 | 0.9245 | 0.9804 | 0.9767 | 0.8277 | 0.6607 | 0.9031 | 0.9836 | 0.7790 | 0.5779 | 0.25 | 0.8791 | 0.9276 | 0.9767 | 0.9660 | 0.9708 | 0.9353 | 0.9749 | 0.9610 | 0.9681 | 0.9611 | 0.9545 | 0.9382 | 0.9185 | 0.9363 | 0.9273 | 0.9903 |
| 0.016 | 12.8205 | 6000 | 0.0436 | 0.9607 | 0.9778 | 0.0 | 0.9516 | 0.9698 | 0.9027 | 0.9274 | 0.9781 | 0.9677 | 0.8472 | 0.6913 | 0.9259 | 0.9877 | 0.7857 | 0.5925 | 0.3819 | 0.8853 | 0.9377 | 0.9731 | 0.9688 | 0.9752 | 0.9461 | 0.9756 | 0.9594 | 0.9613 | 0.9568 | 0.9545 | 0.9447 | 0.9247 | 0.9396 | 0.9321 | 0.9907 |
| 0.012 | 14.9573 | 7000 | 0.0447 | 0.9598 | 0.9808 | 0.0 | 0.9558 | 0.9737 | 0.9070 | 0.9339 | 0.9719 | 0.9654 | 0.8482 | 0.7009 | 0.9280 | 0.9859 | 0.7879 | 0.6027 | 0.4729 | 0.8909 | 0.9413 | 0.9781 | 0.9750 | 0.9740 | 0.9431 | 0.9778 | 0.9624 | 0.9623 | 0.9665 | 0.9555 | 0.9403 | 0.9275 | 0.9413 | 0.9344 | 0.9911 |
| 0.0086 | 17.0940 | 8000 | 0.0487 | 0.9584 | 0.9814 | 0.0 | 0.9529 | 0.9733 | 0.9114 | 0.9350 | 0.9857 | 0.9767 | 0.8493 | 0.7059 | 0.9306 | 0.9829 | 0.7918 | 0.6216 | 0.5292 | 0.8934 | 0.9330 | 0.9790 | 0.9766 | 0.9750 | 0.9395 | 0.9747 | 0.9624 | 0.9605 | 0.9631 | 0.9580 | 0.9399 | 0.9282 | 0.9423 | 0.9352 | 0.9908 |
| 0.0062 | 19.2308 | 9000 | 0.0509 | 0.9596 | 0.9795 | 0.0 | 0.9594 | 0.9708 | 0.9128 | 0.9299 | 0.9872 | 0.9837 | 0.8491 | 0.7188 | 0.9270 | 0.9859 | 0.7923 | 0.6468 | 0.5371 | 0.8919 | 0.9369 | 0.9783 | 0.9756 | 0.9749 | 0.9382 | 0.9778 | 0.9650 | 0.9647 | 0.9653 | 0.9559 | 0.9461 | 0.9337 | 0.9394 | 0.9365 | 0.9911 |
| 0.0045 | 21.3675 | 10000 | 0.0548 | 0.9559 | 0.9774 | 0.0 | 0.9524 | 0.9720 | 0.9080 | 0.9348 | 0.9827 | 0.9814 | 0.8446 | 0.7117 | 0.9271 | 0.9800 | 0.7977 | 0.6428 | 0.5266 | 0.8964 | 0.9351 | 0.9754 | 0.9716 | 0.9728 | 0.9478 | 0.9757 | 0.9584 | 0.9698 | 0.9587 | 0.9548 | 0.9423 | 0.9242 | 0.9457 | 0.9348 | 0.9907 |
| 0.0036 | 23.5043 | 11000 | 0.0560 | 0.9594 | 0.9781 | 0.0 | 0.9575 | 0.9720 | 0.9121 | 0.9367 | 0.9814 | 0.9814 | 0.8504 | 0.7209 | 0.9317 | 0.9807 | 0.7922 | 0.6507 | 0.5918 | 0.8864 | 0.9380 | 0.9769 | 0.9722 | 0.9745 | 0.9399 | 0.9771 | 0.9628 | 0.9675 | 0.9618 | 0.9581 | 0.9446 | 0.9288 | 0.9434 | 0.9361 | 0.9910 |
| 0.0026 | 25.6410 | 12000 | 0.0576 | 0.9596 | 0.9798 | 0.0 | 0.9575 | 0.9732 | 0.9130 | 0.9308 | 0.9831 | 0.9791 | 0.8471 | 0.7104 | 0.9268 | 0.9836 | 0.7967 | 0.6563 | 0.6222 | 0.8982 | 0.9345 | 0.9771 | 0.9739 | 0.9733 | 0.9402 | 0.9771 | 0.9673 | 0.9656 | 0.9642 | 0.9576 | 0.9480 | 0.9288 | 0.9446 | 0.9366 | 0.9910 |
| 0.002 | 27.7778 | 13000 | 0.0608 | 0.9555 | 0.9796 | 0.0 | 0.9561 | 0.9717 | 0.9045 | 0.9319 | 0.9848 | 0.9791 | 0.8488 | 0.7157 | 0.9268 | 0.9852 | 0.7909 | 0.6580 | 0.6039 | 0.8900 | 0.9360 | 0.9802 | 0.9717 | 0.9750 | 0.9361 | 0.9778 | 0.9646 | 0.9683 | 0.9615 | 0.9565 | 0.9465 | 0.9279 | 0.9433 | 0.9355 | 0.9909 |
| 0.0016 | 29.9145 | 14000 | 0.0601 | 0.9573 | 0.9801 | 0.0 | 0.9589 | 0.9722 | 0.9135 | 0.9353 | 0.9848 | 0.9837 | 0.8499 | 0.7202 | 0.9316 | 0.9871 | 0.7942 | 0.6677 | 0.6432 | 0.9017 | 0.9402 | 0.9799 | 0.9744 | 0.9740 | 0.9455 | 0.9783 | 0.9631 | 0.9719 | 0.9645 | 0.9600 | 0.9482 | 0.9331 | 0.9443 | 0.9387 | 0.9913 |
| 0.0013 | 32.0513 | 15000 | 0.0613 | 0.9606 | 0.9798 | 0.0 | 0.9571 | 0.9739 | 0.9155 | 0.9365 | 0.9840 | 0.9791 | 0.8466 | 0.7221 | 0.9321 | 0.9861 | 0.7948 | 0.6622 | 0.6281 | 0.9017 | 0.9407 | 0.9787 | 0.9739 | 0.9740 | 0.9475 | 0.9774 | 0.9632 | 0.9693 | 0.9644 | 0.9594 | 0.9469 | 0.9313 | 0.9457 | 0.9384 | 0.9912 |
| 0.001 | 34.1880 | 16000 | 0.0639 | 0.9611 | 0.9808 | 0.0 | 0.9601 | 0.9729 | 0.9149 | 0.9337 | 0.9867 | 0.9814 | 0.8483 | 0.7269 | 0.9307 | 0.9838 | 0.7956 | 0.6627 | 0.6154 | 0.9025 | 0.9397 | 0.9797 | 0.9733 | 0.9738 | 0.9383 | 0.9776 | 0.9636 | 0.9674 | 0.9637 | 0.9576 | 0.9480 | 0.9304 | 0.9457 | 0.9380 | 0.9911 |
| 0.0009 | 36.3248 | 17000 | 0.0621 | 0.9622 | 0.9811 | 0.0 | 0.9604 | 0.9741 | 0.9156 | 0.9359 | 0.9855 | 0.9814 | 0.8510 | 0.7273 | 0.9319 | 0.9859 | 0.7991 | 0.6646 | 0.6413 | 0.8999 | 0.9393 | 0.9789 | 0.9739 | 0.9740 | 0.9427 | 0.9789 | 0.9653 | 0.9687 | 0.9637 | 0.9597 | 0.9460 | 0.9324 | 0.9456 | 0.9390 | 0.9913 |
| 0.0008 | 38.4615 | 18000 | 0.0631 | 0.9620 | 0.9801 | 0.0 | 0.9582 | 0.9744 | 0.9190 | 0.9350 | 0.9853 | 0.9814 | 0.8514 | 0.7253 | 0.9298 | 0.9848 | 0.7992 | 0.6677 | 0.6434 | 0.8992 | 0.9401 | 0.9797 | 0.9739 | 0.9731 | 0.9421 | 0.9789 | 0.9653 | 0.9681 | 0.9643 | 0.9592 | 0.9462 | 0.9319 | 0.9457 | 0.9388 | 0.9913 |
| 0.0007 | 40.5983 | 19000 | 0.0633 | 0.9615 | 0.9806 | 0.0 | 0.9589 | 0.9741 | 0.9170 | 0.9358 | 0.9861 | 0.9814 | 0.8501 | 0.7268 | 0.9316 | 0.9857 | 0.7973 | 0.6662 | 0.6393 | 0.9002 | 0.9405 | 0.9797 | 0.9745 | 0.9738 | 0.9442 | 0.9796 | 0.9658 | 0.9679 | 0.9644 | 0.9586 | 0.9469 | 0.9323 | 0.9459 | 0.9391 | 0.9913 |
| 0.0008 | 42.7350 | 20000 | 0.0635 | 0.9622 | 0.9808 | 0.0 | 0.9589 | 0.9741 | 0.9176 | 0.9355 | 0.9855 | 0.9814 | 0.8496 | 0.7261 | 0.9306 | 0.9850 | 0.7974 | 0.6672 | 0.6393 | 0.9005 | 0.9405 | 0.9794 | 0.9745 | 0.9738 | 0.9437 | 0.9794 | 0.9651 | 0.9674 | 0.9638 | 0.9587 | 0.9469 | 0.9318 | 0.9460 | 0.9388 | 0.9913 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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