| --- |
| 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. |
