PIIBench Direct Fine-Tuned DeBERTa
This is the final selected PIIBench model: a standard DeBERTa-v3-base token classifier trained directly on the prepared multi-source PII benchmark splits. It outperformed the source-conditioned hierarchical comparison model on the complete held-out experiment test split.
Paper
This model is released with the paper:
Fine-Tuning Over Architectural Complexity: Broad-Coverage PII Detection on PIIBench with DeBERTa
arXiv: https://arxiv.org/abs/2605.25816
Hugging Face Papers: https://huggingface.co/papers/2605.25816
This repository corresponds to the direct fine-tuned DeBERTa model reported as the final selected model in the paper.
Results
The reported evaluation uses the later prepared PIIBench experiment variant
with 82 retained entity types and a held-out test split of 100,002 records.
It is not the earlier 48-type Hub dataset release.
| Held-Out Evaluation | Records | F1 | Precision | Recall |
|---|---|---|---|---|
| Corrected heldout subset | 5,000 | 0.6476 | 0.6300 | 0.6662 |
| Complete experiment test split | 100,002 | 0.6455 | 0.6277 | 0.6645 |
Full-test SHA-256:
65f8edc86399ba3f9e4ba44591d4583f9271f5d1df20e30a913305049559df77
Usage
This is a standard Transformers token-classification model:
from transformers import pipeline
pipe = pipeline(
"token-classification",
model="Pritesh-2711/piibench-deberta-base",
aggregation_strategy="simple",
)
print(pipe("Contact me at jane@example.com."))
Related Resources
- Dataset: Pritesh-2711/pii-bench
- Source-conditioned hierarchical comparison model: Pritesh-2711/piibench-deberta-sch