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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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## 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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### 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 |
