| --- |
| license: other |
| license_name: apmic-proprietary |
| license_link: LICENSE |
| language: |
| - zh |
| - en |
| base_model: |
| - openai/gpt-oss-20b |
| tags: |
| - privacy |
| - pii |
| - pii-detection |
| - redaction |
| - de-identification |
| - traditional-chinese |
| - taiwan |
| - nvidia |
| - enterprise |
| - ace |
| datasets: |
| - nvidia/Nemotron-PII |
| pipeline_tag: text-generation |
| library_name: transformers |
| --- |
| |
| # ACE-privacy-filter-zhtw |
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| ## Model Description |
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| **ACE-privacy-filter-zhtw** is a privacy-preserving language model from APMIC's **ACE** family, engineered to detect, classify, and neutralize **personally identifiable information (PII)** within Traditional Chinese (zh-TW) text. It is the enterprise sibling of an internal research lineage, hardened for production and aligned to the realities of Taiwanese data — government records, financial documents, healthcare notes, and customer correspondence. |
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| The model treats privacy not as a post-processing step, but as a native behavior: given free-form text, it returns content in which sensitive identifiers have been surfaced and removed, while the surrounding meaning is preserved. |
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| It is built on OpenAI's open-weight `gpt-oss-20b`. The corpora behind its zh-TW alignment and the full methodology of its training recipe, however, remain proprietary to APMIC. What is shared here is what it does — not entirely how it came to do it. |
|
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| ## Model Details |
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| - **Developed by:** APMIC |
| - **Funded by:** APMIC, led by CEO Jerry Wu |
| - **Model type:** Causal language model, fine-tuned for privacy filtering / de-identification (Transformers) |
| - **Language(s):** Traditional Chinese (zh-TW) & English |
| - **License:** APMIC proprietary (enterprise use; contact APMIC for terms) |
| - **Base model:** [`openai/gpt-oss-20b`](https://huggingface.co/openai/gpt-oss-20b) — OpenAI's open-weight model, fine-tuned and aligned by APMIC for zh-TW privacy filtering. *(The training recipe and zh-TW alignment corpora remain proprietary.)* |
|
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| ## What It Does |
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| Given Traditional Chinese text, `ACE-privacy-filter-zhtw`: |
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| - **Detects** personally identifiable information embedded in natural, conversational, and document-style language. |
| - **Classifies** each identifier into a privacy category. |
| - **Neutralizes** it — redacting, masking, or replacing the sensitive span while keeping the text readable and semantically intact. |
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| It is designed to operate on the messy, real-world text where regex and rule engines fail: mixed Chinese-English content, inconsistent formatting, OCR-derived noise, and the idiomatic phrasing of Taiwanese business and government communication. |
|
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| ## Privacy Entity Coverage |
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| The filter is tuned toward identifiers that matter in a Taiwanese context, including (but not limited to): |
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| - 身分證字號 (National ID numbers) |
| - 健保卡號 / 病歷號 (NHI card & medical record numbers) |
| - 手機與市話號碼 (Mobile & landline numbers) |
| - 地址 (Residential & mailing addresses) |
| - 銀行帳號與信用卡號 (Bank account & card numbers) |
| - 姓名 (Personal names) |
| - Email 與帳號識別碼 (Email & account identifiers) |
| - 車牌號碼 (Vehicle plate numbers) |
| - 公司統一編號 (Business registration numbers) |
|
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| ## Data Foundation |
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| The structural backbone of `ACE-privacy-filter-zhtw`'s privacy understanding draws on [**nvidia/Nemotron-PII**](https://huggingface.co/datasets/nvidia/Nemotron-PII) — NVIDIA's large-scale synthetic corpus of 100,000 records spanning 55+ PII/PHI categories across 50+ industries, covering both structured documents (forms, invoices) and unstructured content (emails, notes). |
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| This foundation gave the model a broad, industry-spanning prior over *what privacy looks like* — across healthcare, finance, legal, and enterprise scenarios. APMIC then carried that prior across the language boundary, re-grounding it in the entity types, formats, and cultural conventions specific to Traditional Chinese and Taiwan. The bridge from Nemotron-PII's English foundation to native zh-TW behavior is where APMIC's proprietary work lives. |
|
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| ## NVIDIA Ecosystem |
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| `ACE-privacy-filter-zhtw` is part of APMIC's broader collaboration with NVIDIA's data and platform ecosystem. It builds on NVIDIA-originated privacy data, is optimized for inference on modern NVIDIA GPU architectures, and is designed to slot into enterprise deployment pipelines alongside other models in the ACE family. |
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| ## Intended Use |
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| - De-identification of Traditional Chinese documents prior to storage, analytics, or LLM ingestion. |
| - Privacy guardrails in conversational AI and RAG pipelines handling Taiwanese user data. |
| - Compliance support for organizations operating under Taiwan's 個人資料保護法 (Personal Data Protection Act) and adjacent regulatory regimes. |
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| ### Out of Scope |
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| - The model is an **assistive control, not a legal guarantee.** It does not certify compliance, and its output should be reviewed in high-stakes settings. |
| - It is not a general-purpose chat assistant. |
| - Performance on languages or locales outside Traditional Chinese / Taiwan is not a design target. |
|
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| ## Usage |
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| > The input/output format shown below is representative. Production integration details are provided to enterprise partners. |
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|
| ```python |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| |
| model_id = "APMIC/ACE-privacy-filter-zhtw" |
| tokenizer = AutoTokenizer.from_pretrained(model_id) |
| model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto") |
| |
| text = "您好，我是王小明，身分證字號 A123456789，手機 0912-345-678，住台北市信義區市府路1號。" |
| |
| messages = [{"role": "user", "content": text}] |
| inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device) |
| output = model.generate(inputs, max_new_tokens=256) |
| print(tokenizer.decode(output[0], skip_special_tokens=True)) |
| # → 您好，我是[姓名]，身分證字號 [身分證字號]，手機 [電話]，住[地址]。 |
| ``` |
|
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| ## Positioning |
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| `ACE-privacy-filter-zhtw` demonstrates APMIC's capacity to take a foundation of NVIDIA privacy data and forge it into a **Traditional-Chinese-native, enterprise-ready privacy layer** — for organizations that need their data protected before it is ever processed, and who would rather not know exactly how the lock was made. |
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| ## Disclaimer |
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| This model is provided for enterprise privacy-filtering use. No PII detection system is perfect; APMIC makes no warranty that all sensitive information will be identified or removed. Operators remain responsible for validating outputs and meeting their own regulatory obligations. |
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| --- |
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| *© APMIC. Part of the ACE model family.* |
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