Model overview
Fine-tuned `openai/privacy-filter` for **fine-grained PII extraction** across **55 categories** from `nvidia/Nemotron-PII`. - **Base model**: `openai/privacy-filter` — 1.4B-parameter MoE (50M active per token), BIOES token-classification head - **Task**: Token classification for PII detection (BIOES scheme) - **Training data**: Full 100K rows of `nvidia/Nemotron-PII` train split - **Held-out val**: 10K label-stratified rows from the Nemotron `test` split (every label has ≥229 entities) - **Recipe**: `opf train` (OpenAI's official fine-tuning CLI) — full fine-tune, AdamW, lr=1e-4, 5 epochs, bf16, weight decay 0.0 - **Labels**: 55 fine-grained PII categories → 221 BIOES classes (1 `O` + 55 × B/I/E/S) The base model ships with 8 coarse PII categories (`private_person`, `private_email`, etc.). This model trades that coarse vocabulary for a **5× more granular one** — `first_name`, `last_name`, `medical_record_number`, `credit_debit_card`, `ssn`, and so on — matching what downstream redaction and masking pipelines typically need.
Key features
特点摘要待依据来源补齐，不凭模型名称推断能力。
Limitations & considerations
Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.
Source prices & cost analysis
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Estimate monthly base token costs
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Public benchmarks & run configuration
暂无已采集的可核验公开评分。模型未上榜不代表能力较低。
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Sources & traceability
许可证：apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。