File size: 5,294 Bytes
cfc2530 |
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 |
---
license: apache-2.0
base_model: distilbert-base-multilingual-cased
tags:
- generated_from_trainer
model-index:
- name: privacy-masknig
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. -->
# privacy-masknig
This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3686
- Overall Precision: 0.2885
- Overall Recall: 0.2143
- Overall F1: 0.2459
- Overall Accuracy: 0.8688
- Bod F1: 0.2375
- Building F1: 0.2871
- Cardissuer F1: 0.0
- City F1: 0.2540
- Country F1: 0.3055
- Date F1: 0.2341
- Driverlicense F1: 0.2233
- Email F1: 0.2654
- Geocoord F1: 0.1603
- Givenname1 F1: 0.2161
- Givenname2 F1: 0.1507
- Idcard F1: 0.2472
- Ip F1: 0.1851
- Lastname1 F1: 0.2296
- Lastname2 F1: 0.1305
- Lastname3 F1: 0.1245
- Pass F1: 0.1980
- Passport F1: 0.2792
- Postcode F1: 0.2794
- Secaddress F1: 0.2486
- Sex F1: 0.2933
- Socialnumber F1: 0.2258
- State F1: 0.2921
- Street F1: 0.2177
- Tel F1: 0.2409
- Time F1: 0.2893
- Title F1: 0.2814
- Username F1: 0.2368
## 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-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | 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 |
|:-------------:|:-----:|:------:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:------:|:-----------:|:-------------:|:-------:|:----------:|:-------:|:----------------:|:--------:|:-----------:|:-------------:|:-------------:|:---------:|:------:|:------------:|:------------:|:------------:|:-------:|:-----------:|:-----------:|:-------------:|:------:|:---------------:|:--------:|:---------:|:------:|:-------:|:--------:|:-----------:|
| 0.4774 | 1.0 | 62187 | 0.4611 | 0.1764 | 0.1017 | 0.1291 | 0.8380 | 0.1353 | 0.1842 | 0.0 | 0.1255 | 0.2337 | 0.1185 | 0.0936 | 0.1261 | 0.0500 | 0.0893 | 0.0506 | 0.1041 | 0.1122 | 0.1241 | 0.0463 | 0.0020 | 0.0486 | 0.1080 | 0.1726 | 0.1540 | 0.2044 | 0.0886 | 0.1588 | 0.1239 | 0.1406 | 0.1667 | 0.1583 | 0.1386 |
| 0.4205 | 2.0 | 124374 | 0.4272 | 0.2372 | 0.1567 | 0.1887 | 0.8542 | 0.1831 | 0.2706 | 0.0 | 0.1923 | 0.2819 | 0.1821 | 0.1521 | 0.1863 | 0.1197 | 0.0997 | 0.0662 | 0.1473 | 0.1512 | 0.1443 | 0.0955 | 0.0527 | 0.1678 | 0.1997 | 0.2469 | 0.2066 | 0.2641 | 0.1827 | 0.2266 | 0.1602 | 0.1879 | 0.2372 | 0.2202 | 0.2069 |
| 0.3367 | 3.0 | 186561 | 0.3686 | 0.2885 | 0.2143 | 0.2459 | 0.8688 | 0.2375 | 0.2871 | 0.0 | 0.2540 | 0.3055 | 0.2341 | 0.2233 | 0.2654 | 0.1603 | 0.2161 | 0.1507 | 0.2472 | 0.1851 | 0.2296 | 0.1305 | 0.1245 | 0.1980 | 0.2792 | 0.2794 | 0.2486 | 0.2933 | 0.2258 | 0.2921 | 0.2177 | 0.2409 | 0.2893 | 0.2814 | 0.2368 |
| 0.301 | 4.0 | 248748 | 0.3734 | 0.3073 | 0.2484 | 0.2747 | 0.8737 | 0.2565 | 0.3272 | 0.1429 | 0.2634 | 0.3355 | 0.2707 | 0.2591 | 0.3032 | 0.2153 | 0.2458 | 0.1847 | 0.2757 | 0.2252 | 0.2594 | 0.1680 | 0.1551 | 0.2410 | 0.3080 | 0.2945 | 0.2488 | 0.3139 | 0.2522 | 0.3007 | 0.2447 | 0.2584 | 0.3107 | 0.2933 | 0.2880 |
| 0.2451 | 5.0 | 310935 | 0.3895 | 0.3091 | 0.2664 | 0.2862 | 0.8744 | 0.2720 | 0.3313 | 0.0 | 0.2773 | 0.3470 | 0.2803 | 0.2732 | 0.3109 | 0.2202 | 0.2554 | 0.1945 | 0.2899 | 0.2382 | 0.2539 | 0.1800 | 0.1651 | 0.2514 | 0.3156 | 0.2982 | 0.2720 | 0.3364 | 0.2695 | 0.3196 | 0.2561 | 0.2732 | 0.3169 | 0.3054 | 0.3020 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
|