| | --- |
| | 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. |
