| --- |
| language: |
| - en |
| library_name: peft |
| pipeline_tag: text-generation |
| base_model: EleutherAI/pythia-160m |
| base_model_relation: adapter |
| datasets: |
| - cc0de/Enron_email |
| tags: |
| - lora |
| - privacy |
| - pii |
| - membership-inference |
| - masked-fine-tuning |
| --- |
| |
| # TMFT: Targeted Masked Fine-Tuning |
|
|
| This project tests whether masking privacy-sensitive token losses during LoRA |
| fine-tuning reduces PII memorization with less utility degradation than random |
| masking. It is an empirical mitigation study, not differential privacy or |
| machine unlearning. |
|
|
| ## Vessel Setup |
|
|
| Upload the entire `tmft_project/` directory and open a terminal in that |
| directory. |
|
|
| ```bash |
| python -m pip install -U pip |
| python -m pip uninstall -y transformers peft accelerate tokenizers huggingface_hub datasets |
| python -m pip install -r requirements.txt |
| python -m spacy download en_core_web_sm |
| ``` |
|
|
| Restart the Jupyter kernel after installation. The tested compatibility stack |
| uses PyTorch 2.3.1, Transformers 4.41.2, PEFT 0.11.1, and Datasets 2.20.0. |
|
|
| ## End-to-End Experiment |
|
|
| Prepare real PII-containing Enron splits and a real prefix-target evaluation |
| set: |
|
|
| ```bash |
| python main.py --mode prepare --force_prepare |
| ``` |
|
|
| Train all conditions: |
|
|
| ```bash |
| python main.py --mode train --method all |
| ``` |
|
|
| Evaluate TER, SER, held-out perplexity, MDP, Loss-MIA AUC, and Min-K MIA AUC: |
|
|
| ```bash |
| python main.py --mode eval --method all |
| ``` |
|
|
| Generate result figures: |
|
|
| ```bash |
| python main.py --mode plot |
| ``` |
|
|
| The full pipeline can be launched with: |
|
|
| ```bash |
| python main.py --mode all --method all --force_prepare |
| ``` |
|
|
| For an interactive run, execute `tmft_experiment.ipynb` from top to bottom. |
|
|
| ## Experimental Conditions |
|
|
| - `baseline`: standard LoRA fine-tuning |
| - `rmft`: random 15% loss masking |
| - `tmft_ner`: loss masking at spaCy plus regex PII spans |
| - `tmft_mia`: online token masking where the current model is more confident |
| than the frozen base model |
| - `tmft_combined`: union of NER and post-warm-up MIA masks |
|
|
| ## Outputs |
|
|
| - `data/processed/`: train, validation, and test DatasetDict |
| - `data/pii_eval.json`: automatically generated real PII prefix-target attacks |
| - `results/<method>/`: LoRA adapters and training metadata |
| - `results/tables/main_results.csv`: submission-ready numeric table |
| - `results/figures/`: PNG and PDF privacy/utility figures |
|
|
| Do not report results if preprocessing prints a synthetic fallback warning. |
| The final config disables fallback so an unavailable real dataset fails loudly. |
|
|
| ## Hugging Face Upload |
|
|
| ```bash |
| huggingface-cli login |
| python main.py --mode upload --method tmft_combined \ |
| --hf_repo_id YOUR_USERNAME/tmft-pythia-160m-tmft-combined |
| ``` |
|
|
| Use `--public` only after checking that the saved artifacts contain no raw PII. |
