Names, emails, API keys—Our ambassador @povedaai is putting MiniCPM4-0.5B to work detecting sensitive text. 🔐

The project adds a MiniCPM-powered PII recognizer to Microsoft Presidio, helping identify sensitive information in text for downstream anonymization.

Here’s what’s under the hood 👇

🧠 A compact foundation: Built on MiniCPM4-0.5B, adapting a 0.5B-parameter model for a specialized token-classification task.

🛠️ Task-specific fine-tuning: Uses a LoRA adapter trained on NVIDIA’s Nemotron-PII dataset, paired with a custom token-classification head.

🔎 55 PII/PHI labels: Covers information such as names, email addresses, phone numbers, financial identifiers, API keys, passwords, and medical record numbers.

🔗 Presidio integration: Maps model predictions into Presidio’s entity format, allowing the recognizer to work within its analysis pipeline and support downstream anonymization workflows.

💻 Local inference: The recognizer loads the model and adapter locally, offering a path for developers exploring sensitive-text processing in their own environments.

We love seeing the community turn MiniCPM into practical, specialized tools. This project is an example of how open models, lightweight adaptation, and existing developer frameworks can come together for a concrete application. 🌍

Working on data preparation, text anonymization, or privacy tooling? Try the demo, explore the adapter, and check out the implementation! 🚀

🧩 Model: http://huggingface.co/luispoveda93/MiniCPM4-0.5B-PII-tagger-lora
📂 Repo: http://github.com/PovedaAqui/presidio/tree/feature/minicpm-pii-recognizer
🔗 Hugging Face: https://huggingface.co/openbmb/MiniCPM4-0.5B

#MiniCPM #OpenBMB #Privacy #OpenSource #PII