MODEL PROFILE

privacy-filter-multilingual-v2

OpenMed · privacy-filter-multilingual-v2

Fine-tuned `openai/privacy-filter` for **fine-grained PII extraction** across **54 categories** in **16 languages**. This v2 checkpoint is the more performant successor to `OpenMed/privacy-filter-multilingual`, with stronger multilingual PII masking behavior while keeping the same 16-language, fine-grained OpenMed label space and runtime interface. - **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) - **Languages (16)**: Arabic, Bengali, Chinese, Dutch, English, French, German, Hindi, Italian, Japanese, Korean, Portuguese, Spanish, Telugu, Turkish, Vietnamese - **Training data**: The original language-balanced multilingual OpenMed/AI4Privacy mix, followed by a v2 source-balanced privacy-masking adaptation mix from AI4Privacy OpenPII, Nemotron, Gretel, and Privy-style PII data - **Recipe**: `opf train` (OpenAI's official fine-tuning CLI) — full fine-tune, AdamW, balanced language and source sampling, bf16 - **Labels**: 54 PII categories → 217 BIOES classes (1 `O` + 54 × B/I/E/S) The base model ships with 8 coarse PII categories and English-only training. This model trades that for a **6.75× more granular vocabulary** spanning identity, contact, address, financial, vehicle, digital, and crypto labels — all evaluated across 16 languages.

Text analysislicense:apache-2.0
Context windowNot disclosed
Input → OutputText → Text
Model weightsNot verified
Profile updated2026/11/2 19:15:48
OVERVIEW

Model overview

Fine-tuned `openai/privacy-filter` for **fine-grained PII extraction** across **54 categories** in **16 languages**. This v2 checkpoint is the more performant successor to `OpenMed/privacy-filter-multilingual`, with stronger multilingual PII masking behavior while keeping the same 16-language, fine-grained OpenMed label space and runtime interface. - **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) - **Languages (16)**: Arabic, Bengali, Chinese, Dutch, English, French, German, Hindi, Italian, Japanese, Korean, Portuguese, Spanish, Telugu, Turkish, Vietnamese - **Training data**: The original language-balanced multilingual OpenMed/AI4Privacy mix, followed by a v2 source-balanced privacy-masking adaptation mix from AI4Privacy OpenPII, Nemotron, Gretel, and Privy-style PII data - **Recipe**: `opf train` (OpenAI's official fine-tuning CLI) — full fine-tune, AdamW, balanced language and source sampling, bf16 - **Labels**: 54 PII categories → 217 BIOES classes (1 `O` + 54 × B/I/E/S) The base model ships with 8 coarse PII categories and English-only training. This model trades that for a **6.75× more granular vocabulary** spanning identity, contact, address, financial, vehicle, digital, and crypto labels — all evaluated across 16 languages.

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

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PRICING & COST

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INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

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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。