| --- |
| license: apache-2.0 |
| base_model: bert-base-cased |
| tags: |
| - PII |
| - NER |
| - Bert |
| - Token Classification |
| datasets: |
| - generator |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| model-index: |
| - name: pii_model |
| results: |
| - task: |
| name: Token Classification |
| type: token-classification |
| dataset: |
| name: generator |
| type: generator |
| config: default |
| split: train |
| args: default |
| metrics: |
| - name: Precision |
| type: precision |
| value: 0.954751 |
| - name: Recall |
| type: recall |
| value: 0.965233 |
| - name: F1 |
| type: f1 |
| value: 0.959964 |
| - name: Accuracy |
| type: accuracy |
| value: 0.991199 |
| pipeline_tag: token-classification |
| language: |
| - en |
| --- |
| |
| <!-- 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. --> |
|
|
| # Personal Identifiable Information (PII Model) |
|
|
| This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the generator dataset. |
| It achieves the following results: |
|
|
| - Training Loss: 0.003900 |
| - Validation Loss: 0.051071 |
| - Precision: 95.53% |
| - Recall: 96.60% |
| - F1: 96% |
| - Accuracy:99.11% |
|
|
| ## Model description |
|
|
| Meet our digital safeguard, a savvy token classification model with a knack for spotting personally identifiable information (PII) entities. Trained on the illustrious Bert architecture and fine-tuned on a custom dataset, this model is like a superhero for privacy, swiftly detecting names, addresses, dates of birth, and more. With each token it encounters, it acts as a vigilant guardian, ensuring that sensitive information remains shielded from prying eyes, making the digital realm a safer and more secure place to explore. |
|
|
| ## Model can Detect Following Entity Group |
|
|
| - ACCOUNTNUMBER |
| - FIRSTNAME |
| - ACCOUNTNAME |
| - PHONENUMBER |
| - CREDITCARDCVV |
| - CREDITCARDISSUER |
| - PREFIX |
| - LASTNAME |
| - AMOUNT |
| - DATE |
| - DOB |
| - COMPANYNAME |
| - BUILDINGNUMBER |
| - STREET |
| - SECONDARYADDRESS |
| - STATE |
| - EMAIL |
| - CITY |
| - CREDITCARDNUMBER |
| - SSN |
| - URL |
| - USERNAME |
| - PASSWORD |
| - COUNTY |
| - PIN |
| - MIDDLENAME |
| - IBAN |
| - GENDER |
| - AGE |
| - ZIPCODE |
| - SEX |
|
|
|
|
|
|
|
|
| ### Training hyperparameters |
| The following hyperparameters were used during training: |
|
|
| | Hyperparameter | Value | |
| |------------------------------|---------------| |
| | Learning Rate | 5e-5 | |
| | Train Batch Size | 16 | |
| | Eval Batch Size | 16 | |
| | Number of Training Epochs | 7 | |
| | Weight Decay | 0.01 | |
| | Save Strategy | Epoch | |
| | Load Best Model at End | True | |
| | Metric for Best Model | F1 | |
| | Push to Hub | True | |
| | Evaluation Strategy | Epoch | |
| | Early Stopping Patience | 3 | |
|
|
|
|
| ### Training results |
|
|
| | Epoch | Training Loss | Validation Loss | Precision (%) | Recall (%) | F1 Score (%) | Accuracy (%) | |
| |-------|---------------|-----------------|---------------|------------|--------------|--------------| |
| | 1 | 0.0443 | 0.038108 | 91.88 | 95.17 | 93.50 | 98.80 | |
| | 2 | 0.0318 | 0.035728 | 94.13 | 96.15 | 95.13 | 98.90 | |
| | 3 | 0.0209 | 0.032016 | 94.81 | 96.42 | 95.61 | 99.01 | |
| | 4 | 0.0154 | 0.040221 | 93.87 | 95.80 | 94.82 | 98.88 | |
| | 5 | 0.0084 | 0.048183 | 94.21 | 96.06 | 95.13 | 98.93 | |
| | 6 | 0.0037 | 0.052281 | 94.49 | 96.60 | 95.53 | 99.07 | |
|
|
|
|
|
|
|
|
|
|
|
|
| ### Author |
| Abhijeet Santosh Lokhande |
| abhijeetlokhande1996@gmail.com | https://www.linkedin.com/in/ablds/ | https://github.com/abhijeetscode |
|
|
| ### Framework versions |
|
|
| - Transformers 4.38.2 |
| - Pytorch 2.1.0+cu121 |
| - Datasets 2.18.0 |
| - Tokenizers 0.15.2 |
