Model save

README.md

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+---
+library_name: transformers
+license: apache-2.0
+base_model: distilbert-base-uncased
+tags:
+- generated_from_trainer
+model-index:
+- name: PII-Detection-V2.1
+  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. -->
+
+# PII-Detection-V2.1
+
+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: 0.0368
+- Overall Precision: 0.9351
+- Overall Recall: 0.9512
+- Overall F1: 0.9431
+- Overall Accuracy: 0.9916
+- Accountname F1: 0.9871
+- Accountnumber F1: 0.9910
+- Buildingnumber F1: 0.7900
+- City F1: 0.9636
+- Companyname F1: 0.9590
+- County F1: 0.9427
+- Creditcardcvv F1: 0.8543
+- Creditcardissuer F1: 0.9043
+- Creditcardnumber F1: 0.8696
+- Email F1: 0.9979
+- Firstname F1: 0.9195
+- Fullname F1: 0.9831
+- Iban F1: 0.9658
+- Lastname F1: 0.8370
+- Middlename F1: 0.8452
+- Name F1: 0.9926
+- Number F1: 0.9157
+- Phonenumber F1: 0.9792
+- Pin F1: 0.8959
+- Secondaryaddress F1: 0.9787
+- State F1: 0.9286
+- Street F1: 0.8457
+- Streetaddress F1: 0.7259
+- Url F1: 0.9980
+- Username F1: 0.9509
+
+## 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: 32
+- eval_batch_size: 32
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 5
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | Accountname F1 | Accountnumber F1 | Buildingnumber F1 | City F1 | Companyname F1 | County F1 | Creditcardcvv F1 | Creditcardissuer F1 | Creditcardnumber F1 | Email F1 | Firstname F1 | Fullname F1 | Iban F1 | Lastname F1 | Middlename F1 | Name F1 | Number F1 | Phonenumber F1 | Pin F1 | Secondaryaddress F1 | State F1 | Street F1 | Streetaddress F1 | Url F1 | Username F1 |
+|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:--------------:|:----------------:|:-----------------:|:-------:|:--------------:|:---------:|:----------------:|:-------------------:|:-------------------:|:--------:|:------------:|:-----------:|:-------:|:-----------:|:-------------:|:-------:|:---------:|:--------------:|:------:|:-------------------:|:--------:|:---------:|:----------------:|:------:|:-----------:|
+| 0.0885        | 1.0   | 1361 | 0.0489          | 0.8436            | 0.8993         | 0.8705     | 0.9846           | 0.9573         | 0.9269           | 0.6452            | 0.9295  | 0.8956         | 0.8195    | 0.7291           | 0.8274              | 0.7974              | 0.9931   | 0.8206       | 0.9634      | 0.9363  | 0.7408      | 0.4829        | 0.9732  | 0.6385    | 0.9037         | 0.7326 | 0.9648              | 0.8559   | 0.6673    | 0.0246           | 0.9901 | 0.9369      |
+| 0.0338        | 2.0   | 2722 | 0.0345          | 0.9069            | 0.9333         | 0.9199     | 0.9898           | 0.9667         | 0.9713           | 0.7312            | 0.9521  | 0.9363         | 0.9009    | 0.8538           | 0.9093              | 0.8471              | 0.9970   | 0.8864       | 0.9819      | 0.9136  | 0.7901      | 0.7787        | 0.9864  | 0.8854    | 0.9589         | 0.8097 | 0.9732              | 0.9124   | 0.7673    | 0.5046           | 0.9975 | 0.9530      |
+| 0.0172        | 3.0   | 4083 | 0.0313          | 0.9277            | 0.9439         | 0.9358     | 0.9908           | 0.9889         | 0.9727           | 0.7592            | 0.9611  | 0.9551         | 0.9357    | 0.8656           | 0.9076              | 0.8629              | 0.9982   | 0.9062       | 0.9813      | 0.9710  | 0.8314      | 0.8107        | 0.9913  | 0.9435    | 0.9727         | 0.8847 | 0.9779              | 0.9173   | 0.7895    | 0.6915           | 0.9980 | 0.9521      |
+| 0.0103        | 4.0   | 5444 | 0.0342          | 0.9330            | 0.9493         | 0.9411     | 0.9913           | 0.9895         | 0.9910           | 0.7939            | 0.9633  | 0.9490         | 0.9365    | 0.8577           | 0.9051              | 0.8660              | 0.9996   | 0.9149       | 0.9833      | 0.9753  | 0.8289      | 0.8430        | 0.9899  | 0.8456    | 0.9588         | 0.8824 | 0.9795              | 0.9299   | 0.8361    | 0.7332           | 0.9980 | 0.9527      |
+| 0.0058        | 5.0   | 6805 | 0.0368          | 0.9351            | 0.9512         | 0.9431     | 0.9916           | 0.9871         | 0.9910           | 0.7900            | 0.9636  | 0.9590         | 0.9427    | 0.8543           | 0.9043              | 0.8696              | 0.9979   | 0.9195       | 0.9831      | 0.9658  | 0.8370      | 0.8452        | 0.9926  | 0.9157    | 0.9792         | 0.8959 | 0.9787              | 0.9286   | 0.8457    | 0.7259           | 0.9980 | 0.9509      |
+
+
+### Framework versions
+
+- Transformers 4.45.2
+- Pytorch 2.2.0
+- Datasets 3.0.1
+- Tokenizers 0.20.1

model.safetensors

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