Instructions to use Musayusuf001/pii-detection-distilbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Musayusuf001/pii-detection-distilbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Musayusuf001/pii-detection-distilbert")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Musayusuf001/pii-detection-distilbert") model = AutoModelForTokenClassification.from_pretrained("Musayusuf001/pii-detection-distilbert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
PII NER Fine-tuned DistilBERT
Overview
Fine-tuned DistilBERT model for detecting Personally Identifiable Information (PII).
Use Cases
- PII detection
- Data redaction
- Privacy filtering
Model Type
- Token Classification (NER)
- PyTorch fine-tuned model
Training Dataset
English AI4Privacy Dataset
The model was fine-tuned using the English AI4Privacy dataset containing synthetic text annotated with privacy-related entities. https://www.kaggle.com/datasets/verracodeguacas/ai4privacy-pii
Usage
from transformers import pipeline
pipe = pipeline( "token-classification", model="samuelolubukun/pii-ner-finetuned-distilbert" )
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