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bohrariyanshi/
pii-ner-extraction

named-entity-recognition

google-bert/bert-base-multilingual-cased

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Multilingual NER Model for PII Detection

This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the WikiANN dataset for Named Entity Recognition (NER).

Model Description

Developed by: bohrariyanshi
Model type: Token Classification (NER)
Language(s): Multilingual (primarily English)
Base model: bert-base-multilingual-cased

Intended Uses & Limitations

Intended Uses

Named Entity Recognition for Person (PER), Organization (ORG), and Location (LOC)
Text analysis and information extraction
PII (Personally Identifiable Information) detection

Limitations

Trained on WikiANN (multilingual) but evaluated primarily on English subsets
May have lower performance on non-English text
Limited to PER, ORG, LOC entity types

Training Data

The model was fine-tuned on the WikiANN dataset:

Training examples: 20,000
Validation examples: 10,000
Test examples: 10,000
Entity types: PER (Person), ORG (Organization), LOC (Location)

Training Procedure

Training Hyperparameters

Learning rate: 2e-5
Training epochs: 3
Batch size: 16
Max sequence length: 256
Optimizer: AdamW
Weight decay: 0.01

Performance

The model achieves high confidence predictions on standard NER tasks:

High confidence predictions (>90%): 19/21 entities in test cases
Average inference time: ~264ms per sentence
Entity types detected: PER, ORG, LOC with high accuracy

Usage

python

from transformers import pipeline

# Load the model
ner = pipeline("ner", model="bohrariyanshi/pii-ner-extraction", aggregation_strategy="simple")

# Example usage
text = "Barack Obama was born in Hawaii."
entities = ner(text)
print(entities)
# Output: [{'entity_group': 'PER', 'score': 0.968, 'word': 'Barack Obama', 'start': 0, 'end': 12}, ...]

Model Architecture

Base: BERT-base-multilingual-cased
Parameters: 177M
Architecture: Transformer with token classification head
Task: Named Entity Recognition (NER)

Evaluation Results

The model demonstrates superior performance compared to base BERT:

Confident predictions: 19 high-confidence entities vs 0 for base BERT
Precision: High accuracy in entity detection
Speed: ~264ms per sentence (acceptable for production use)

Environmental Impact

Training was performed on a Google Colab T4 GPU for a short duration (fine-tuning only).
The overall environmental impact is minimal compared to large-scale pretraining runs.

Citation

If you use this model, please cite:

bibtex

@model{bohrariyanshi-pii-ner-extraction,
  author = {bohrariyanshi},
  title = {Multilingual NER Model for PII Detection},
  year = {2025},
  url = {https://huggingface.co/bohrariyanshi/pii-ner-extraction}
}