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