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README.md +122 -0
all_results.json +74 -0
eval_results.json +68 -0
train_results.json +9 -0
trainer_state.json +1060 -0

README.md ADDED Viewed

	@@ -0,0 +1,122 @@

+---
+license: mit
+base_model: microsoft/mdeberta-v3-base
+tags:
+- generated_from_trainer
+model-index:
+- name: mdeberta-v3-base_finetuned_ai4privacy_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. -->
+# mdeberta-v3-base_finetuned_ai4privacy_v2
+This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0323
+- Overall Precision: 0.9636
+- Overall Recall: 0.9731
+- Overall F1: 0.9683
+- Overall Accuracy: 0.9896
+- Accountname F1: 0.9998
+- Accountnumber F1: 0.9973
+- Age F1: 0.9878
+- Amount F1: 0.9495
+- Bic F1: 0.9932
+- Bitcoinaddress F1: 0.9704
+- Buildingnumber F1: 0.9648
+- City F1: 0.9887
+- Companyname F1: 0.9942
+- County F1: 0.9940
+- Creditcardcvv F1: 0.9820
+- Creditcardissuer F1: 0.9985
+- Creditcardnumber F1: 0.9570
+- Currency F1: 0.8750
+- Currencycode F1: 0.9888
+- Currencyname F1: 0.7416
+- Currencysymbol F1: 0.9819
+- Date F1: 0.9295
+- Dob F1: 0.8946
+- Email F1: 0.9998
+- Ethereumaddress F1: 0.9965
+- Eyecolor F1: 0.9984
+- Firstname F1: 0.9886
+- Gender F1: 0.9962
+- Height F1: 1.0
+- Iban F1: 0.9966
+- Ip F1: 0.6284
+- Ipv4 F1: 0.8884
+- Ipv6 F1: 0.8015
+- Jobarea F1: 0.9940
+- Jobtitle F1: 0.9973
+- Jobtype F1: 0.9970
+- Lastname F1: 0.9653
+- Litecoinaddress F1: 0.9109
+- Mac F1: 0.9992
+- Maskednumber F1: 0.9524
+- Middlename F1: 0.9347
+- Nearbygpscoordinate F1: 1.0
+- Ordinaldirection F1: 0.9984
+- Password F1: 0.9936
+- Phoneimei F1: 0.9998
+- Phonenumber F1: 0.9992
+- Pin F1: 0.9857
+- Prefix F1: 0.9801
+- Secondaryaddress F1: 0.9988
+- Sex F1: 0.9979
+- Ssn F1: 0.9983
+- State F1: 0.9944
+- Street F1: 0.9953
+- Time F1: 0.9974
+- Url F1: 1.0
+- Useragent F1: 1.0
+- Username F1: 0.9966
+- Vehiclevin F1: 0.9936
+- Vehiclevrm F1: 0.9917
+- Zipcode F1: 0.9727
+## 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: 16
+- eval_batch_size: 16
+- 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: 5
+### 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 |
+|:-------------:|:-----:|:-----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:--------------:|:----------------:|:------:|:---------:|:------:|:-----------------:|:-----------------:|:-------:|:--------------:|:---------:|:----------------:|:-------------------:|:-------------------:|:-----------:|:---------------:|:---------------:|:-----------------:|:-------:|:------:|:--------:|:------------------:|:-----------:|:------------:|:---------:|:---------:|:-------:|:------:|:-------:|:-------:|:----------:|:-----------:|:----------:|:-----------:|:------------------:|:------:|:---------------:|:-------------:|:----------------------:|:-------------------:|:-----------:|:------------:|:--------------:|:------:|:---------:|:-------------------:|:------:|:------:|:--------:|:---------:|:-------:|:------:|:------------:|:-----------:|:-------------:|:-------------:|:----------:|
+| 0.0622        | 1.0   | 10463 | 0.0541          | 0.9247            | 0.9384         | 0.9315     | 0.9770           | 0.9949         | 0.9917           | 0.9812 | 0.9224    | 0.9847 | 0.9592            | 0.9056            | 0.9595  | 0.9802         | 0.9775    | 0.9350           | 0.9971              | 0.8939              | 0.7380      | 0.9664          | 0.0843          | 0.9721            | 0.7784  | 0.6363 | 0.9993   | 0.9877             | 0.9833      | 0.9696       | 0.9866    | 0.9716    | 0.9914  | 0.0    | 0.8238  | 0.8025  | 0.9882     | 0.9874      | 0.9878     | 0.8820      | 0.9085             | 0.9869 | 0.8831          | 0.8686        | 0.9984                 | 0.9958              | 0.9786      | 0.9971       | 0.9885         | 0.9482 | 0.9455    | 0.9934              | 0.9956 | 0.9860 | 0.9673   | 0.9799    | 0.9916  | 0.9958 | 0.9995       | 0.9875      | 0.9555        | 0.9819        | 0.9311     |
+| 0.0492        | 2.0   | 20926 | 0.0445          | 0.9376            | 0.9494         | 0.9434     | 0.9788           | 0.9970         | 0.9979           | 0.9883 | 0.9492    | 0.9949 | 0.9626            | 0.9548            | 0.9819  | 0.9911         | 0.9922    | 0.9740           | 0.9985              | 0.9057              | 0.5805      | 0.9771          | 0.4872          | 0.9734            | 0.8257  | 0.7479 | 0.9989   | 0.9944             | 0.9960      | 0.9819       | 0.9933    | 0.9958    | 0.9962  | 0.1521 | 0.7969  | 0.8083  | 0.9957     | 0.9972      | 0.9970     | 0.9335      | 0.8953             | 0.9967 | 0.8786          | 0.9232        | 1.0                    | 0.9980              | 0.9868      | 0.9985       | 0.9967         | 0.9808 | 0.9708    | 0.9969              | 0.9979 | 0.9980 | 0.9921   | 0.9913    | 0.9979  | 1.0    | 1.0          | 0.9914      | 0.9788        | 0.9859        | 0.9622     |
+| 0.0392        | 3.0   | 31389 | 0.0395          | 0.9458            | 0.9584         | 0.9521     | 0.9815           | 0.9988         | 0.9971           | 0.9861 | 0.9573    | 0.9955 | 0.9764            | 0.9518            | 0.9836  | 0.9914         | 0.9921    | 0.9802           | 0.9985              | 0.9300              | 0.8115      | 0.9862          | 0.5935          | 0.9821            | 0.8684  | 0.7870 | 0.9994   | 0.9972             | 0.9976      | 0.9860       | 0.9956    | 0.9981    | 0.9964  | 0.3002 | 0.8302  | 0.7274  | 0.9863     | 0.9968      | 0.9979     | 0.9528      | 0.9208             | 0.9881 | 0.9195          | 0.9269        | 0.9992                 | 0.9980              | 0.9894      | 0.9998       | 0.9984         | 0.9750 | 0.9767    | 0.9971              | 0.9972 | 0.9978 | 0.9927   | 0.9952    | 0.9966  | 1.0    | 0.9997       | 0.9962      | 0.9942        | 0.9937        | 0.9653     |
+| 0.0311        | 4.0   | 41852 | 0.0341          | 0.9537            | 0.9667         | 0.9601     | 0.9855           | 0.9998         | 0.9986           | 0.9869 | 0.9545    | 0.9961 | 0.9774            | 0.9632            | 0.9885  | 0.9952         | 0.9940    | 0.9838           | 0.9985              | 0.9392              | 0.8567      | 0.9863          | 0.7015          | 0.9853            | 0.9158  | 0.8687 | 1.0      | 0.9979             | 0.9984      | 0.9875       | 0.9971    | 1.0       | 0.9962  | 0.4882 | 0.8664  | 0.6966  | 0.9927     | 0.9973      | 0.9967     | 0.9583      | 0.9251             | 0.9979 | 0.9275          | 0.9351        | 1.0                    | 0.9988              | 0.9932      | 0.9998       | 0.9980         | 0.9852 | 0.9760    | 0.9983              | 0.9980 | 0.9985 | 0.9932   | 0.9952    | 0.9966  | 0.9996 | 1.0          | 0.9969      | 0.9955        | 0.9942        | 0.9685     |
+| 0.0196        | 5.0   | 52315 | 0.0323          | 0.9636            | 0.9731         | 0.9683     | 0.9896           | 0.9998         | 0.9973           | 0.9878 | 0.9495    | 0.9932 | 0.9704            | 0.9648            | 0.9887  | 0.9942         | 0.9940    | 0.9820           | 0.9985              | 0.9570              | 0.8750      | 0.9888          | 0.7416          | 0.9819            | 0.9295  | 0.8946 | 0.9998   | 0.9965             | 0.9984      | 0.9886       | 0.9962    | 1.0       | 0.9966  | 0.6284 | 0.8884  | 0.8015  | 0.9940     | 0.9973      | 0.9970     | 0.9653      | 0.9109             | 0.9992 | 0.9524          | 0.9347        | 1.0                    | 0.9984              | 0.9936      | 0.9998       | 0.9992         | 0.9857 | 0.9801    | 0.9988              | 0.9979 | 0.9983 | 0.9944   | 0.9953    | 0.9974  | 1.0    | 1.0          | 0.9966      | 0.9936        | 0.9917        | 0.9727     |
+### Framework versions
+- Transformers 4.35.2
+- Pytorch 2.1.0+cu121
+- Datasets 2.16.1
+- Tokenizers 0.15.0

all_results.json ADDED Viewed

	@@ -0,0 +1,74 @@

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+}

eval_results.json ADDED Viewed

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train_results.json ADDED Viewed

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+}

trainer_state.json ADDED Viewed

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