tanchao.xyz

Customize OpenAI privacy-filter for Snowflake semantic_categories

Active

Fine-tune OpenAI's privacy-filter model on Snowflake's semantic_categories taxonomy, then evaluate against a hand-labeled holdout.

Started · Updated
data-governancellmsnowflakeevals

Overview

OpenAI’s privacy-filter model is trained on generic PII categories. Snowflake’s semantic_categories use a richer, domain-specific taxonomy (e.g. US_PASSPORT, IBAN_CODE, HEALTHCARE_NUMBER).

Goal: Customize the privacy-filter so it can output Snowflake-aligned categories directly, then evaluate quality against a hand-labeled holdout set.

Success criteria:

  • Macro-F1 ≥ 0.80 on the holdout across all Snowflake categories that have ≥ 20 labeled examples.
  • Latency p95 ≤ 150 ms per document at evaluation time.

Resources

2026-05-06 — kickoff

  • Pulled the full Snowflake semantic_categories reference list (47 categories as of Snowflake 8.x).
  • Mapped each Snowflake category to the closest OpenAI privacy-filter output label where one exists. ~30% have no direct mapping — these are the interesting gap cases.
  • Next: sample the gap categories from internal datasets and build a label schema for annotation.

2026-04-23 — initial exploration

Fine-tuning pipeline

  • opf train ships natively — handles JSONL ingestion, 128-token banded attention windowing, AdamW with gradient accumulation, checkpoint serialization.
  • Two demo workflows: policy adaptation (relabel existing categories) and new taxonomy (custom label space).
  • Output head remapping: exact-match labels copy weights directly; new labels warm-start from closest base class (e.g. B-custom_id inherits B-ID weights). This is why OpenAI’s benchmark jumped from 54% → 96% F1 on small data.
  • Custom taxonomies configured via label_space.json with span_class_names. BIOES expansion is automatic; background class O must be first entry.

Compute requirements

  • Model: 2.8 GB safetensors (BF16), 1.5B total params / 50M active (MoE).
  • Full fine-tune: ~24 GB VRAM (mixed precision) — single A100 40GB or RTX 4090 24GB.
  • LoRA alternative: ~12 GB, viable on consumer GPUs.

PoC plan (one-week target)

  1. Environment + baseline — clone repo, pull checkpoint (~17 GB), verify opf redact on sample text, download ai4privacy English splits, convert to opf eval JSONL.
  2. Baseline eval — collapse ai4privacy’s 27 classes → Privacy Filter’s 8 categories, run opf eval, reproduce ~96% F1 as sanity check, note per-category breakdown.
  3. Taxonomy design — design ~15–20 target categories aligned to Snowflake’s SEMANTIC_CATEGORY (NAME, EMAIL, PAYMENT_CARD, PASSPORT, NATIONAL_IDENTIFIER, STREET_ADDRESS, PHONE_NUMBER, IP_ADDRESS, DATE_OF_BIRTH, AGE, GENDER, OCCUPATION, SALARY, MEDICAL_CONDITION, MEDICATION…). Write label_space.json, maintain a mapping file.
  4. Data prep + smoke training — generate JSONL with new labels, 80/10/10 split, run opf train on 5–10k subset to validate pipeline end-to-end.
  5. Full fine-tune — ~150k examples, 2–3 epochs, single A100, checkpoint every N steps.
  6. Evaluation — opf eval on held-out test, build confusion matrix. Focus on overlapping pairs: NAME vs ORGANIZATION_IDENTIFIER, NATIONAL_IDENTIFIER vs TAX_IDENTIFIER, PAYMENT_CARD vs BANK_ACCOUNT.

Key risks

  • Category overlap degradation — expect 10–20 pt F1 drop on overlapping numeric-ID categories.
  • Label skew — ai4privacy over-represents firstname; eval needs stratified sampling to avoid misleading macro-F1.
  • OOD gap — ai4privacy covers education/health/psychology/finance; Snowflake customer data (log lines, transaction records, support tickets) may differ.
  • Licensing — ai4privacy is academic-friendly; production use at Snowflake needs commercial license or synthetic dataset alternative.

← All projects