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---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-three_v2
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-uncased-three_v2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0646
- Overall Precision: 0.8692
- Overall Recall: 0.9049
- Overall F1: 0.8867
- Overall Accuracy: 0.9812
- Accountname F1: 0.9773
- Accountnumber F1: 0.9883
- Age F1: 0.8924
- Amount F1: 0.9622
- Bic F1: 0.8699
- Bitcoinaddress F1: 0.9496
- Buildingnumber F1: 0.9553
- City F1: 0.9484
- Companyname F1: 0.7802
- County F1: 0.9546
- Creditcardcvv F1: 0.8307
- Creditcardissuer F1: 0.9822
- Creditcardnumber F1: 0.8662
- Currency F1: 0.7573
- Currencycode F1: 0.7819
- Currencyname F1: 0.0943
- Currencysymbol F1: 0.8871
- Date F1: 0.8276
- Dob F1: 0.8951
- Email F1: 0.9543
- Ethereumaddress F1: 1.0
- Eyecolor F1: 0.9344
- Firstname F1: 0.8670
- Gender F1: 0.9632
- Height F1: 0.9716
- Iban F1: 0.9468
- Ip F1: 0.8493
- Ipv4 F1: 0.8556
- Ipv6 F1: 0.6738
- Jobarea F1: 0.8987
- Jobtitle F1: 0.9577
- Jobtype F1: 0.9303
- Lastname F1: 0.8520
- Litecoinaddress F1: 0.8554
- Mac F1: 0.9928
- Maskednumber F1: 0.8369
- Middlename F1: 0.7513
- Nearbygpscoordinate F1: 0.9269
- Ordinaldirection F1: 0.9817
- Password F1: 0.8792
- Phoneimei F1: 0.9887
- Phonenumber F1: 0.9230
- Pin F1: 0.8550
- Prefix F1: 0.9199
- Secondaryaddress F1: 0.9740
- Sex F1: 0.9667
- Ssn F1: 0.9150
- State F1: 0.9738
- Street F1: 0.8667
- Time F1: 0.9069
- Url F1: 0.9878
- Useragent F1: 0.9943
- Username F1: 0.8805
- Vehiclevin F1: 0.9620
- Vehiclevrm F1: 0.9821
- Zipcode F1: 0.9423
## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 7
### Training results
| Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | Accountname F1 | Accountnumber F1 | Age F1 | Amount F1 | Bic F1 | Bitcoinaddress F1 | Buildingnumber F1 | City F1 | Companyname F1 | County F1 | Creditcardcvv F1 | Creditcardissuer F1 | Creditcardnumber F1 | Currency F1 | Currencycode F1 | Currencyname F1 | Currencysymbol F1 | Date F1 | Dob F1 | Email F1 | Ethereumaddress F1 | Eyecolor F1 | Firstname F1 | Gender F1 | Height F1 | Iban F1 | Ip F1 | Ipv4 F1 | Ipv6 F1 | Jobarea F1 | Jobtitle F1 | Jobtype F1 | Lastname F1 | Litecoinaddress F1 | Mac F1 | Maskednumber F1 | Middlename F1 | Nearbygpscoordinate F1 | Ordinaldirection F1 | Password F1 | Phoneimei F1 | Phonenumber F1 | Pin F1 | Prefix F1 | Secondaryaddress F1 | Sex F1 | Ssn F1 | State F1 | Street F1 | Time F1 | Url F1 | Useragent F1 | Username F1 | Vehiclevin F1 | Vehiclevrm F1 | Zipcode F1 |
|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:--------------:|:----------------:|:------:|:---------:|:------:|:-----------------:|:-----------------:|:-------:|:--------------:|:---------:|:----------------:|:-------------------:|:-------------------:|:-----------:|:---------------:|:---------------:|:-----------------:|:-------:|:------:|:--------:|:------------------:|:-----------:|:------------:|:---------:|:---------:|:-------:|:------:|:-------:|:-------:|:----------:|:-----------:|:----------:|:-----------:|:------------------:|:------:|:---------------:|:-------------:|:----------------------:|:-------------------:|:-----------:|:------------:|:--------------:|:------:|:---------:|:-------------------:|:------:|:------:|:--------:|:---------:|:-------:|:------:|:------------:|:-----------:|:-------------:|:-------------:|:----------:|
| 0.2314 | 1.0 | 1299 | 0.1322 | 0.7381 | 0.8044 | 0.7698 | 0.9699 | 0.8367 | 0.8496 | 0.6806 | 0.5746 | 0.3430 | 0.8041 | 0.8363 | 0.8420 | 0.6640 | 0.3719 | 0.0357 | 0.4493 | 0.4554 | 0.5 | 0.0 | 0.0 | 0.4768 | 0.7367 | 0.8515 | 0.9459 | 0.9908 | 0.1847 | 0.75 | 0.6004 | 0.6974 | 0.7952 | 0.8253 | 0.8246 | 0.7153 | 0.1512 | 0.7939 | 0.4468 | 0.7328 | 0.0 | 0.9026 | 0.4479 | 0.1935 | 0.8676 | 0.4604 | 0.7485 | 0.9364 | 0.8741 | 0.0060 | 0.8869 | 0.8963 | 0.8999 | 0.8115 | 0.8037 | 0.7648 | 0.8413 | 0.9516 | 0.9388 | 0.8054 | 0.3968 | 0.4866 | 0.8015 |
| 0.0799 | 2.0 | 2598 | 0.0761 | 0.8402 | 0.8811 | 0.8602 | 0.9782 | 0.9717 | 0.9659 | 0.8657 | 0.9269 | 0.8091 | 0.9468 | 0.9322 | 0.9342 | 0.7616 | 0.9097 | 0.7692 | 0.9821 | 0.7663 | 0.7208 | 0.5127 | 0.0248 | 0.8171 | 0.7995 | 0.8872 | 0.9503 | 0.9985 | 0.8933 | 0.8138 | 0.9242 | 0.9632 | 0.9326 | 0.8285 | 0.8610 | 0.5590 | 0.8205 | 0.9382 | 0.9207 | 0.8141 | 0.8254 | 0.9928 | 0.7820 | 0.5016 | 0.8957 | 0.9611 | 0.8398 | 0.9838 | 0.9222 | 0.7815 | 0.9156 | 0.9650 | 0.9534 | 0.9125 | 0.9593 | 0.8373 | 0.8884 | 0.9804 | 0.9806 | 0.8505 | 0.9056 | 0.9592 | 0.9209 |
| 0.0604 | 3.0 | 3897 | 0.0681 | 0.8591 | 0.8945 | 0.8765 | 0.9799 | 0.9829 | 0.9882 | 0.8940 | 0.9528 | 0.8575 | 0.9588 | 0.9468 | 0.9462 | 0.7795 | 0.9411 | 0.8156 | 0.9821 | 0.8064 | 0.7556 | 0.7036 | 0.0317 | 0.8614 | 0.8159 | 0.8861 | 0.9547 | 0.9969 | 0.9311 | 0.8487 | 0.9505 | 0.9718 | 0.9388 | 0.8284 | 0.7476 | 0.7306 | 0.8966 | 0.9562 | 0.9323 | 0.8372 | 0.8718 | 0.9928 | 0.8047 | 0.6657 | 0.9103 | 0.9769 | 0.8639 | 0.9937 | 0.9262 | 0.8207 | 0.9170 | 0.9737 | 0.9663 | 0.9201 | 0.9705 | 0.8333 | 0.9061 | 0.9915 | 0.9909 | 0.8685 | 0.9808 | 0.9671 | 0.9289 |
| 0.0469 | 4.0 | 5196 | 0.0646 | 0.8692 | 0.9049 | 0.8867 | 0.9812 | 0.9773 | 0.9883 | 0.8924 | 0.9622 | 0.8699 | 0.9496 | 0.9553 | 0.9484 | 0.7802 | 0.9546 | 0.8307 | 0.9822 | 0.8662 | 0.7573 | 0.7819 | 0.0943 | 0.8871 | 0.8276 | 0.8951 | 0.9543 | 1.0 | 0.9344 | 0.8670 | 0.9632 | 0.9716 | 0.9468 | 0.8493 | 0.8556 | 0.6738 | 0.8987 | 0.9577 | 0.9303 | 0.8520 | 0.8554 | 0.9928 | 0.8369 | 0.7513 | 0.9269 | 0.9817 | 0.8792 | 0.9887 | 0.9230 | 0.8550 | 0.9199 | 0.9740 | 0.9667 | 0.9150 | 0.9738 | 0.8667 | 0.9069 | 0.9878 | 0.9943 | 0.8805 | 0.9620 | 0.9821 | 0.9423 |
| 0.0365 | 5.0 | 6495 | 0.0688 | 0.8757 | 0.9066 | 0.8909 | 0.9815 | 0.9877 | 0.9891 | 0.8980 | 0.9720 | 0.8845 | 0.9588 | 0.9591 | 0.9511 | 0.8042 | 0.9555 | 0.8268 | 0.9870 | 0.8498 | 0.7164 | 0.7954 | 0.2376 | 0.9093 | 0.8287 | 0.8881 | 0.9543 | 1.0 | 0.9392 | 0.8648 | 0.9776 | 0.9771 | 0.9657 | 0.8602 | 0.8201 | 0.7170 | 0.9104 | 0.9594 | 0.9336 | 0.8596 | 0.8698 | 0.9928 | 0.8396 | 0.7732 | 0.9266 | 0.9837 | 0.8613 | 0.9799 | 0.9370 | 0.8524 | 0.9232 | 0.9724 | 0.9713 | 0.9317 | 0.9748 | 0.8684 | 0.9145 | 0.9897 | 0.9909 | 0.8822 | 0.9620 | 0.9594 | 0.9490 |
| 0.0277 | 6.0 | 7794 | 0.0766 | 0.8745 | 0.9120 | 0.8929 | 0.9813 | 0.9877 | 0.9900 | 0.8945 | 0.9746 | 0.8930 | 0.9434 | 0.9551 | 0.9471 | 0.7986 | 0.96 | 0.8303 | 0.9853 | 0.8607 | 0.7025 | 0.8011 | 0.2532 | 0.9124 | 0.8337 | 0.8961 | 0.9518 | 0.9985 | 0.9396 | 0.8713 | 0.9748 | 0.9801 | 0.9543 | 0.8564 | 0.8474 | 0.7036 | 0.9193 | 0.9567 | 0.9320 | 0.8612 | 0.8450 | 0.9952 | 0.8282 | 0.7761 | 0.9323 | 0.9839 | 0.8869 | 0.9925 | 0.9387 | 0.8360 | 0.9268 | 0.9723 | 0.9702 | 0.9372 | 0.9712 | 0.8759 | 0.9190 | 0.9859 | 0.9909 | 0.8835 | 0.9511 | 0.9697 | 0.9430 |
| 0.0225 | 7.0 | 9093 | 0.0850 | 0.8795 | 0.9113 | 0.8951 | 0.9819 | 0.9896 | 0.9936 | 0.8952 | 0.9746 | 0.8916 | 0.9545 | 0.9595 | 0.9505 | 0.8040 | 0.9581 | 0.8362 | 0.9919 | 0.8677 | 0.7402 | 0.7932 | 0.2246 | 0.9097 | 0.8339 | 0.8964 | 0.9503 | 1.0 | 0.9396 | 0.8748 | 0.9796 | 0.9829 | 0.9604 | 0.8682 | 0.8297 | 0.7449 | 0.9147 | 0.9594 | 0.9281 | 0.8581 | 0.8466 | 0.9952 | 0.8331 | 0.7916 | 0.9373 | 0.9861 | 0.8877 | 0.9925 | 0.9406 | 0.8546 | 0.9289 | 0.9733 | 0.9718 | 0.9330 | 0.9756 | 0.8799 | 0.9217 | 0.9868 | 0.9932 | 0.8802 | 0.9593 | 0.9622 | 0.9459 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.2+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
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