Tanny03/adapterops-pii

adapterops-pii

Lists the personal information in a text as LABEL: value lines, over 19 labels.

Part of AdapterOps: four LoRA adapters over one Qwen2.5-1.5B base, served together with vLLM multi-LoRA. Portfolio project — no real users or customer data.

The scores below describe revision `5315b1dcd98663c2ce7292299cff2e511639480a` (adapter weights sha256 66631c40fcc17994…), the revision the project serves. Load that revision rather than main.

Prompt

List every piece of personal information in the text, one per line, as LABEL: value.
Text: {text}
Found:

Raw text, no chat template. Greedy decoding, at most 384 new tokens. Replace {text} with the input.

Evaluation

Golden sets are frozen random held-out splits; every system below was run on the same items. The hard-cases split is mined from this adapter's own failures, so it is report-only and sits near zero by construction for classification.

Latency with all four adapters served at once on one A10 (vLLM, concurrency 16): P50 858 ms · P95 2,259 ms, measured with the previous revision.

Caveats

• —Trained with PII-free sentences and empty answers so it can report nothing; on 928 PII-free texts it still reports a span in 4. The previous revision, trained only on documents containing PII, reported one in every text.
• —Trained and evaluated on synthetic spans only. Not a compliance control.
• —Strict scoring requires each value to match its span exactly.

Training

QLoRA (4-bit NF4) on Qwen/Qwen2.5-1.5B-Instruct, LoRA rank 16, alpha 32, on all attention and MLP projections; prompt tokens masked from the loss. 13,910 training rows from ai4privacy/pii-masking-openpii-1m (cc-by-4.0).

Full decision log, results and negative findings: https://github.com/tpawar03/AdapterOps.