File size: 4,663 Bytes
bda96d1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 |
---
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
|