Token Classification
Transformers
Safetensors
English
bert
PII
NER
Bert
Token Classification
Eval Results (legacy)
# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("ankitcodes/pii_model")
model = AutoModelForTokenClassification.from_pretrained("ankitcodes/pii_model")Quick Links
Model can Detect Following Entity Group
- ACCOUNTNUMBER
- FIRSTNAME
- ACCOUNTNAME
- PHONENUMBER
- CREDITCARDCVV
- CREDITCARDISSUER
- PREFIX
- LASTNAME
- AMOUNT
- DATE
- DOB
- COMPANYNAME
- BUILDINGNUMBER
- STREET
- SECONDARYADDRESS
- STATE
- CITY
- CREDITCARDNUMBER
- SSN
- URL
- USERNAME
- PASSWORD
- COUNTY
- PIN
- MIDDLENAME
- IBAN
- GENDER
- AGE
- ZIPCODE
- SEX
Framework versions
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
- Downloads last month
- 14
Model tree for ankitcodes/pii_model
Base model
google-bert/bert-base-casedEvaluation results
- Precision on generatorself-reported0.955
- Recall on generatorself-reported0.965
- F1 on generatorself-reported0.960
- Accuracy on generatorself-reported0.991
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="ankitcodes/pii_model")