Measured on Samsung's CredData, split by repository so 154 repos stay unseen. Alone, the model fully masks 22% of credentials: fine on passwords (61%) and tokens (56%), near zero on private keys, JWTs and bare keys assigned in code. With PrivAiTe's rules (0.8.1) the minilm preset reaches 35%, against 60% for the default onnx preset; the gap is almost all generic keys and secrets under arbitrary names (7% vs 34%, 13% vs 86%), where the Privacy Filter model is much stronger. Two fine-tunes closed much of it but flagged far more placeholders, so I didn't ship them. The breakdown is on the card now.
crp4222
crp4222
AI & ML interests
None yet
Recent Activity
repliedto their post about 23 hours ago
I trained a small PII detector for PrivAiTe, my local proxy that masks personal data before a prompt reaches an LLM: crp4222/privaite-pii-minilm.
It is a multilingual MiniLM (117M parameters, 15 types from names and contacts to IDs, bank details and secrets), trained only on datasets whose licenses allow commercial use, released under BSD 3-Clause, with PyTorch, ONNX fp32 and int8, and Core ML builds.
On 9 public test sets it never saw, it masks as many person names as the best of GLiNER PII, NVIDIA GLiNER-PII, GLiNER2, Piiranha and OpenAI privacy-filter, or more, on 6 of them, usually with higher precision. It trails GLiNER PII on short lowercase voice requests and NVIDIA on court rulings.
It is also 2.5 to 7 times smaller and much faster: 9 ms per 1,000 characters on an M1 Pro Neural Engine and 23 ms on CPU in int8, against 186 to 621 ms for the others on the same machine and documents. Numbers, test sets and limits are on the card: https://huggingface.co/crp4222/privaite-pii-minilm updated a model about 23 hours ago
crp4222/privaite-pii-minilm repliedto their post 3 days ago
I trained a small PII detector for PrivAiTe, my local proxy that masks personal data before a prompt reaches an LLM: crp4222/privaite-pii-minilm.
It is a multilingual MiniLM (117M parameters, 15 types from names and contacts to IDs, bank details and secrets), trained only on datasets whose licenses allow commercial use, released under BSD 3-Clause, with PyTorch, ONNX fp32 and int8, and Core ML builds.
On 9 public test sets it never saw, it masks as many person names as the best of GLiNER PII, NVIDIA GLiNER-PII, GLiNER2, Piiranha and OpenAI privacy-filter, or more, on 6 of them, usually with higher precision. It trails GLiNER PII on short lowercase voice requests and NVIDIA on court rulings.
It is also 2.5 to 7 times smaller and much faster: 9 ms per 1,000 characters on an M1 Pro Neural Engine and 23 ms on CPU in int8, against 186 to 621 ms for the others on the same machine and documents. Numbers, test sets and limits are on the card: https://huggingface.co/crp4222/privaite-pii-minilmOrganizations
None yet
replied to their post about 23 hours ago
updated a
model about 23 hours ago
replied to their post 3 days ago
Core ML fp16 on the Neural Engine is tied with fp32 on all nine sets, within 0.1 point of persons fully masked. int8 is the build that loses a little: 0.4 to 3 points on five of the nine, most on the short voice requests (MASSIVE 80.6 against 83.6). So short lowercase requests stay the main weak spot after conversion; int8 makes them slightly worse, Core ML does not. I'll add the per-build table to the card.
posted an update 4 days ago
Post
2,722
I trained a small PII detector for PrivAiTe, my local proxy that masks personal data before a prompt reaches an LLM: crp4222/privaite-pii-minilm.
It is a multilingual MiniLM (117M parameters, 15 types from names and contacts to IDs, bank details and secrets), trained only on datasets whose licenses allow commercial use, released under BSD 3-Clause, with PyTorch, ONNX fp32 and int8, and Core ML builds.
On 9 public test sets it never saw, it masks as many person names as the best of GLiNER PII, NVIDIA GLiNER-PII, GLiNER2, Piiranha and OpenAI privacy-filter, or more, on 6 of them, usually with higher precision. It trails GLiNER PII on short lowercase voice requests and NVIDIA on court rulings.
It is also 2.5 to 7 times smaller and much faster: 9 ms per 1,000 characters on an M1 Pro Neural Engine and 23 ms on CPU in int8, against 186 to 621 ms for the others on the same machine and documents. Numbers, test sets and limits are on the card: crp4222/privaite-pii-minilm
It is a multilingual MiniLM (117M parameters, 15 types from names and contacts to IDs, bank details and secrets), trained only on datasets whose licenses allow commercial use, released under BSD 3-Clause, with PyTorch, ONNX fp32 and int8, and Core ML builds.
On 9 public test sets it never saw, it masks as many person names as the best of GLiNER PII, NVIDIA GLiNER-PII, GLiNER2, Piiranha and OpenAI privacy-filter, or more, on 6 of them, usually with higher precision. It trails GLiNER PII on short lowercase voice requests and NVIDIA on court rulings.
It is also 2.5 to 7 times smaller and much faster: 9 ms per 1,000 characters on an M1 Pro Neural Engine and 23 ms on CPU in int8, against 186 to 621 ms for the others on the same machine and documents. Numbers, test sets and limits are on the card: crp4222/privaite-pii-minilm
- 5 replies
published a
model 4 days ago
liked a
model 22 days ago
liked a
dataset 7 months ago
liked 2
models almost 2 years ago
