For a task like PII (Personally Identifiable Information) data redaction, the training objectives and methodology should focus on identifying and masking sensitive information in text. Below are the most relevant training objectives and approaches for your use case:
- Purpose: Teach the model to identify spans of text corresponding to PII entities (e.g., names, addresses, SSNs, credit card numbers).
- Method:
- Frame the problem as a token classification task with labels such as
O(non-PII),B-PII(beginning of PII entity), andI-PII(inside a PII entity). - Fine-tune a pretrained model (e.g., BERT, DistilBERT, or Flan-T5) on labeled data.
- Frame the problem as a token classification task with labels such as
- Purpose: Formulate the task as a sequence-to-sequence problem where the input sentence contains PII, and the output is the redacted sentence.
- Example:
- Input:
"John Doe lives at 123 Main St." - Output:
"<PII> lives at <PII>."
- Input:
- Advantages:
- Leverages generative models like Flan-T5 or T5 for flexible masking strategies.
- Allows the inclusion of formatting rules or pseudonymization in the output.
- Purpose: Adapt an MLM objective where PII tokens are masked in the input, and the model learns to predict the masked tokens.
- Example:
- Input:
"John [MASK] lives at [MASK]." - Target:
"John Doe lives at 123 Main St."
- Input:
- Modification for PII:
- Instead of predicting tokens, the model predicts a
MASKplaceholder for PII tokens.
- Instead of predicting tokens, the model predicts a
- Purpose: Train the model to predict the start and end positions of PII spans in the input and mask them.
- Example:
- Input:
"Jane's phone number is 555-1234." - Target:
"Jane's phone number is <MASK>."
- Input:
- Purpose: Train the model to redact only specific PII types based on instructions or conditions.
- Example:
- Input:
"Mask phone numbers: Jane's phone number is 555-1234." - Output:
"Mask phone numbers: Jane's phone number is <MASK>."
- Input:
You will need a labeled dataset with annotations for PII entities. Examples:
- PII Types: Names, addresses, phone numbers, emails, credit card numbers, SSNs, etc.
- Data Sources: Use synthetic data, publicly available datasets (e.g., CoNLL-2003 for NER tasks), or real-world text anonymized manually.
- Augmentation:
- Generate synthetic examples with diverse PII formats (e.g.,
[email protected],123-456-7890). - Add variations in entity positions and sentence structures.
- Generate synthetic examples with diverse PII formats (e.g.,
- Suitable for identifying and masking PII spans.
- Examples:
- BERT
- RoBERTa
- DistilBERT
- Better for text-to-text tasks where the output needs formatting or masking replacements.
- Examples:
- T5
- Flan-T5
- GPT-based models
- Use token classification to detect PII spans, then replace them programmatically with placeholders or pseudonyms.
- Fine-tune a pretrained language model on labeled PII data.
- Use a standard classification loss (e.g., cross-entropy).
- Fine-tune generative models with input-output sentence pairs.
- Use sequence-to-sequence loss (e.g., cross-entropy on output tokens).
- Frame as a QA-like task where the model predicts spans for PII entities.
- Precision, Recall, F1-Score: Evaluate the model's ability to correctly identify PII entities.
- Mask Accuracy: Measure how accurately the model applies masks to PII spans.
- Redacted Text Quality: Check for grammatical correctness and meaningful context in the redacted sentences.
- Start with a token classification model like BERT or RoBERTa for simplicity.
- Gradually experiment with text-to-text models (e.g., Flan-T5) for flexibility in masking and formatting.
- Use synthetic data augmentation to ensure robust performance on diverse inputs.
This setup ensures your model is well-suited for PII redaction tasks while maintaining flexibility and accuracy.