update model card README.md

README.md

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+---
+license: apache-2.0
+tags:
+- generated_from_trainer
+model-index:
+- name: bert-base-uncased_ai4privacy_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. -->
+
+# bert-base-uncased_ai4privacy_en
+
+This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0702
+- Overall Precision: 0.9259
+- Overall Recall: 0.9464
+- Overall F1: 0.9360
+- Overall Accuracy: 0.9706
+- Accountname F1: 0.9924
+- Accountnumber F1: 0.9905
+- Age F1: 0.9339
+- Amount F1: 0.9278
+- Bic F1: 0.9598
+- Bitcoinaddress F1: 0.9801
+- Buildingnumber F1: 0.9091
+- City F1: 0.9564
+- Companyname F1: 0.9908
+- County F1: 0.9853
+- Creditcardcvv F1: 0.9639
+- Creditcardissuer F1: 0.9868
+- Creditcardnumber F1: 0.8929
+- Currency F1: 0.7726
+- Currencycode F1: 0.8608
+- Currencyname F1: 0.3650
+- Currencysymbol F1: 0.9536
+- Date F1: 0.8590
+- Dob F1: 0.6490
+- Email F1: 0.9945
+- Ethereumaddress F1: 0.9986
+- Eyecolor F1: 0.9688
+- Firstname F1: 0.9790
+- Gender F1: 0.9832
+- Height F1: 0.9906
+- Iban F1: 1.0
+- Ip F1: 0.1025
+- Ipv4 F1: 0.8217
+- Ipv6 F1: 0.7506
+- Jobarea F1: 0.9306
+- Jobtitle F1: 0.9938
+- Jobtype F1: 0.9508
+- Lastname F1: 0.9480
+- Litecoinaddress F1: 0.9345
+- Mac F1: 1.0
+- Maskednumber F1: 0.8609
+- Middlename F1: 0.9601
+- Nearbygpscoordinate F1: 1.0
+- Ordinaldirection F1: 0.9784
+- Password F1: 0.9839
+- Phoneimei F1: 0.9986
+- Phonenumber F1: 0.9903
+- Pin F1: 0.9390
+- Prefix F1: 0.9441
+- Secondaryaddress F1: 0.9945
+- Sex F1: 0.9780
+- Ssn F1: 0.9898
+- State F1: 0.9805
+- Street F1: 0.9693
+- Time F1: 0.9843
+- Url F1: 0.9984
+- Useragent F1: 0.9918
+- Username F1: 0.9909
+- Vehiclevin F1: 0.9856
+- Vehiclevrm F1: 0.9653
+- Zipcode F1: 0.8990
+
+## 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: 8
+- eval_batch_size: 16
+- 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: 2
+
+### 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 |
+|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:--------------:|:----------------:|:------:|:---------:|:------:|:-----------------:|:-----------------:|:-------:|:--------------:|:---------:|:----------------:|:-------------------:|:-------------------:|:-----------:|:---------------:|:---------------:|:-----------------:|:-------:|:------:|:--------:|:------------------:|:-----------:|:------------:|:---------:|:---------:|:-------:|:------:|:-------:|:-------:|:----------:|:-----------:|:----------:|:-----------:|:------------------:|:------:|:---------------:|:-------------:|:----------------------:|:-------------------:|:-----------:|:------------:|:--------------:|:------:|:---------:|:-------------------:|:------:|:------:|:--------:|:---------:|:-------:|:------:|:------------:|:-----------:|:-------------:|:-------------:|:----------:|
+| 0.0939        | 1.0   | 4350 | 0.0801          | 0.8951            | 0.9309         | 0.9126     | 0.9666           | 0.9840         | 0.9896           | 0.9182 | 0.8769    | 0.9127 | 0.9627            | 0.8770            | 0.9616  | 0.9847         | 0.9712    | 0.9373           | 0.9813              | 0.8406              | 0.3934      | 0.7451          | 0.1372          | 0.9266            | 0.8354  | 0.5796 | 0.9920   | 0.9877             | 0.9037      | 0.9642       | 0.9789    | 0.9906    | 0.9874  | 0.0    | 0.8416  | 0.8087  | 0.8854     | 0.9825      | 0.9426     | 0.9213      | 0.9015             | 0.9806 | 0.7978          | 0.9543        | 1.0                    | 0.9828              | 0.9689      | 0.9917       | 0.9777         | 0.8764 | 0.9340    | 0.9913              | 0.9761 | 0.9949 | 0.9553   | 0.9561    | 0.9723  | 0.9921 | 0.9906       | 0.9779      | 0.9942        | 0.9684        | 0.8522     |
+| 0.0644        | 2.0   | 8700 | 0.0702          | 0.9259            | 0.9464         | 0.9360     | 0.9706           | 0.9924         | 0.9905           | 0.9339 | 0.9278    | 0.9598 | 0.9801            | 0.9091            | 0.9564  | 0.9908         | 0.9853    | 0.9639           | 0.9868              | 0.8929              | 0.7726      | 0.8608          | 0.3650          | 0.9536            | 0.8590  | 0.6490 | 0.9945   | 0.9986             | 0.9688      | 0.9790       | 0.9832    | 0.9906    | 1.0     | 0.1025 | 0.8217  | 0.7506  | 0.9306     | 0.9938      | 0.9508     | 0.9480      | 0.9345             | 1.0    | 0.8609          | 0.9601        | 1.0                    | 0.9784              | 0.9839      | 0.9986       | 0.9903         | 0.9390 | 0.9441    | 0.9945              | 0.9780 | 0.9898 | 0.9805   | 0.9693    | 0.9843  | 0.9984 | 0.9918       | 0.9909      | 0.9856        | 0.9653        | 0.8990     |
+
+
+### Framework versions
+
+- Transformers 4.26.1
+- Pytorch 2.0.0.post101
+- Datasets 2.10.1
+- Tokenizers 0.13.3