Token Classification
Transformers
Safetensors
English
bert
PII
NER
Bert
Token Classification
Eval Results (legacy)
Instructions to use ankitcodes/pii_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ankitcodes/pii_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="ankitcodes/pii_model")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("ankitcodes/pii_model") model = AutoModelForTokenClassification.from_pretrained("ankitcodes/pii_model") - Notebooks
- Google Colab
- Kaggle
| 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. --> | |
| ## 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 |