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--- |
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license: apache-2.0 |
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base_model: bert-base-cased |
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tags: |
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- PII |
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- NER |
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- Bert |
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- Token Classification |
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datasets: |
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- generator |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: pii_model |
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results: |
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- task: |
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name: Token Classification |
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type: token-classification |
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dataset: |
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name: generator |
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type: generator |
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config: default |
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split: train |
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args: default |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.954751 |
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- name: Recall |
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type: recall |
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value: 0.965233 |
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- name: F1 |
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type: f1 |
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value: 0.959964 |
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- name: Accuracy |
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type: accuracy |
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value: 0.991199 |
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pipeline_tag: token-classification |
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language: |
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- en |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# Personal Identifiable Information (PII Model) |
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This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the generator dataset. |
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It achieves the following results: |
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- Training Loss: 0.003900 |
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- Validation Loss: 0.051071 |
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- Precision: 95.53% |
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- Recall: 96.60% |
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- F1: 96% |
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- Accuracy:99.11% |
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## Model description |
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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. |
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## Model can Detect Following Entity Group |
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- ACCOUNTNUMBER |
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- FIRSTNAME |
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- ACCOUNTNAME |
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- PHONENUMBER |
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- CREDITCARDCVV |
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- CREDITCARDISSUER |
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- PREFIX |
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- LASTNAME |
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- AMOUNT |
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- DATE |
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- DOB |
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- COMPANYNAME |
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- BUILDINGNUMBER |
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- STREET |
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- SECONDARYADDRESS |
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- STATE |
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- EMAIL |
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- CITY |
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- CREDITCARDNUMBER |
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- SSN |
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- URL |
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- USERNAME |
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- PASSWORD |
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- COUNTY |
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- PIN |
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- MIDDLENAME |
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- IBAN |
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- GENDER |
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- AGE |
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- ZIPCODE |
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- SEX |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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| Hyperparameter | Value | |
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|------------------------------|---------------| |
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| Learning Rate | 5e-5 | |
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| Train Batch Size | 16 | |
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| Eval Batch Size | 16 | |
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| Number of Training Epochs | 7 | |
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| Weight Decay | 0.01 | |
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| Save Strategy | Epoch | |
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| Load Best Model at End | True | |
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| Metric for Best Model | F1 | |
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| Push to Hub | True | |
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| Evaluation Strategy | Epoch | |
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| Early Stopping Patience | 3 | |
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### Training results |
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| Epoch | Training Loss | Validation Loss | Precision (%) | Recall (%) | F1 Score (%) | Accuracy (%) | |
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|-------|---------------|-----------------|---------------|------------|--------------|--------------| |
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| 1 | 0.0443 | 0.038108 | 91.88 | 95.17 | 93.50 | 98.80 | |
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| 2 | 0.0318 | 0.035728 | 94.13 | 96.15 | 95.13 | 98.90 | |
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| 3 | 0.0209 | 0.032016 | 94.81 | 96.42 | 95.61 | 99.01 | |
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| 4 | 0.0154 | 0.040221 | 93.87 | 95.80 | 94.82 | 98.88 | |
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| 5 | 0.0084 | 0.048183 | 94.21 | 96.06 | 95.13 | 98.93 | |
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| 6 | 0.0037 | 0.052281 | 94.49 | 96.60 | 95.53 | 99.07 | |
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### Author |
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abhijeet[email protected] |
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### Framework versions |
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- Transformers 4.38.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
