---
dataset_info:
  features:
  - name: lang
    dtype: string
  - name: content
    dtype: string
  - name: id
    dtype: int64
  - name: pii
    dtype: string
  splits:
  - name: filtered
    num_bytes: 221082330
    num_examples: 17678
  download_size: 0
  dataset_size: 221082330
---
# Pseudo-labeled-python-data-pii-detection-filtered

This dataset was used for the training of a PII detection NER model. We annotated it using pseudo-labelelling to enhance model performance on some rare PII entities like keys.

It consists of 18,000 files annotates using an ensemble of two encoder models Deberta-v3-large and stanford-deidentifier-base which were fine-tuned on a labeled PII dataset for code with 400 files from this work. To select good-quality pseudo-labels, 
we computed the average probability logits between the models and filtered based on a minimum score. After inspection, we observed a high rate of false positives for Keys and Passwords, hence we retained only the entities that had a trigger word like key, auth and pwd in the surrounding context.